Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Z. Qazi, I. Qazi
Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers. In this paper, we perform the first independent, empirical analysis of Google’s Web Light service to shed light on these concerns. Through a combination of experiments with thousands of real Web Light pages as well as controlled experiments with synthetic Web Light pages, we (i) deconstruct how Web Light modifies webpages, (ii) investigate how ads are shown on Web Light and which ad networks are supported, (iii) measure and compare Web Light’s page load performance, (iv) discuss privacy concerns for users and publishers and (v) investigate the potential use of Web Light as a censorship circumvention tool.
Web Light是谷歌推出的一项转码服务，为在缓慢的移动客户端上搜索的用户显示更轻、更快的网页。该服务检测速度较慢的客户端(例如2G用户)，并尝试将网页动态转换为针对这些客户端优化的版本。Web Light声称可以显著减少页面加载时间，节省用户数据，并大大增加此类网页的流量。然而，围绕这项服务存在一些问题，包括它在保留页面上相关内容、显示第三方广告、提高用户性能以及用户和发布者的隐私问题方面的有效性。在本文中，我们对Google的Web Light服务进行了首次独立的实证分析，以阐明这些问题。通过对数千个真实Web Light页面的实验，以及对合成Web Light页面的对照实验，我们(i)解构Web Light如何修改网页，(ii)调查广告如何在Web Light上显示，以及支持哪些广告网络，(iii)测量和比较Web Light的页面加载性能，(iv)讨论用户和出版商的隐私问题，以及(v)调查Web Light作为审查规避工具的潜在用途。
{"title":"Deconstructing Google’s Web Light Service","authors":"Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Z. Qazi, I. Qazi","doi":"10.1145/3366423.3380168","DOIUrl":"https://doi.org/10.1145/3366423.3380168","url":null,"abstract":"Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers. In this paper, we perform the first independent, empirical analysis of Google’s Web Light service to shed light on these concerns. Through a combination of experiments with thousands of real Web Light pages as well as controlled experiments with synthetic Web Light pages, we (i) deconstruct how Web Light modifies webpages, (ii) investigate how ads are shown on Web Light and which ad networks are supported, (iii) measure and compare Web Light’s page load performance, (iv) discuss privacy concerns for users and publishers and (v) investigate the potential use of Web Light as a censorship circumvention tool.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85962422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Idan Szpektor, Deborah Cohen, G. Elidan, Michael Fink, A. Hassidim, Orgad Keller, Sayalı, Kulkarni, E. Ofek, S. Pudinsky, Asaf Revach, Shimi Salant
We study conversational domain exploration (CODEX), where the user’s goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant . As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system.
{"title":"Dynamic Composition for Conversational Domain Exploration","authors":"Idan Szpektor, Deborah Cohen, G. Elidan, Michael Fink, A. Hassidim, Orgad Keller, Sayalı, Kulkarni, E. Ofek, S. Pudinsky, Asaf Revach, Shimi Salant","doi":"10.1145/3366423.3380167","DOIUrl":"https://doi.org/10.1145/3366423.3380167","url":null,"abstract":"We study conversational domain exploration (CODEX), where the user’s goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant . As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80124187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Jenkins, Jennifer Zhao, Heath Vinicombe, Anant Subramanian, Arun Prasad, Atillia Dobi, E. Li, Yunsong Guo
Understanding content at scale is a difficult but important problem for many platforms. Many previous studies focus on content understanding to optimize engagement with existing users. However, little work studies how to leverage better content understanding to attract new users. In this work, we build a framework for generating natural language content annotations and show how they can be used for search engine optimization. The proposed framework relies on an XGBoost model that labels “pins” with high probability phrases, and a logistic regression layer that learns to rank aggregated annotations for groups of content. The pipeline identifies keywords that are descriptive and contextually meaningful. We perform a large-scale production experiment deployed on the Pinterest platform and show that natural language annotations cause a 1-2% increase in traffic from leading search engines. This increase is statistically significant. Finally, we explore and interpret the characteristics of our annotations framework.
{"title":"Natural Language Annotations for Search Engine Optimization","authors":"P. Jenkins, Jennifer Zhao, Heath Vinicombe, Anant Subramanian, Arun Prasad, Atillia Dobi, E. Li, Yunsong Guo","doi":"10.1145/3366423.3380049","DOIUrl":"https://doi.org/10.1145/3366423.3380049","url":null,"abstract":"Understanding content at scale is a difficult but important problem for many platforms. Many previous studies focus on content understanding to optimize engagement with existing users. However, little work studies how to leverage better content understanding to attract new users. In this work, we build a framework for generating natural language content annotations and show how they can be used for search engine optimization. The proposed framework relies on an XGBoost model that labels “pins” with high probability phrases, and a logistic regression layer that learns to rank aggregated annotations for groups of content. The pipeline identifies keywords that are descriptive and contextually meaningful. We perform a large-scale production experiment deployed on the Pinterest platform and show that natural language annotations cause a 1-2% increase in traffic from leading search engines. This increase is statistically significant. Finally, we explore and interpret the characteristics of our annotations framework.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76820148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, K. Tan
Entity matching (EM) is a classic research problem that identifies data instances referring to the same real-world entity. Recent technical trend in this area is to take advantage of deep learning (DL) to automatically extract discriminative features. DeepER and DeepMatcher have emerged as two pioneering DL models for EM. However, these two state-of-the-art solutions simply incorporate vanilla RNNs and straightforward attention mechanisms. In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea is further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments with 7 benchmark datasets that are publicly accessible. The experimental results clearly establish our superiority over DeepER and DeepMatcher in all the datasets.
{"title":"Multi-Context Attention for Entity Matching","authors":"Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, K. Tan","doi":"10.1145/3366423.3380017","DOIUrl":"https://doi.org/10.1145/3366423.3380017","url":null,"abstract":"Entity matching (EM) is a classic research problem that identifies data instances referring to the same real-world entity. Recent technical trend in this area is to take advantage of deep learning (DL) to automatically extract discriminative features. DeepER and DeepMatcher have emerged as two pioneering DL models for EM. However, these two state-of-the-art solutions simply incorporate vanilla RNNs and straightforward attention mechanisms. In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea is further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments with 7 benchmark datasets that are publicly accessible. The experimental results clearly establish our superiority over DeepER and DeepMatcher in all the datasets.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82690086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, L. Khan
Deep Neural Networks (DNNs) have primarily been demonstrated to be useful for closed-world classification problems where the number of categories is fixed. However, DNNs notoriously fail when tasked with label prediction in a non-stationary data stream scenario, which has the continuous emergence of the unknown or novel class (categories not in the training set). For example, new topics continually emerge in social media or e-commerce. To solve this challenge, a DNN should not only be able to detect the novel class effectively but also incrementally learn new concepts from limited samples over time. Literature that addresses both problems simultaneously is limited. In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories. Different from existing approaches that rely on abundant labeled instances to re-train/update the model, we propose a new approach based on Few Sample and Adversarial Representation Learning (FSAR). The key novelty is that we introduce the adversarial confusion term into both the representation learning and few-sample learning process, which reduces the over-confidence of the model on the seen classes, further enhance the generalization of the model to detect and learn new categories with only a few samples. We train the FSAR operated in two stages: first, FSAR learns an intra-class compacted and inter-class separated feature embedding to detect the novel classes; next, we collect a few labeled samples belong to the new categories, utilize episode-training to exploit the intrinsic features for few-sample learning. We evaluated FSAR on different datasets, using extensive experimental results from various simulated stream benchmarks to show that FSAR effectively outperforms current state-of-the-art approaches.
{"title":"Few-Sample and Adversarial Representation Learning for Continual Stream Mining","authors":"Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, L. Khan","doi":"10.1145/3366423.3380153","DOIUrl":"https://doi.org/10.1145/3366423.3380153","url":null,"abstract":"Deep Neural Networks (DNNs) have primarily been demonstrated to be useful for closed-world classification problems where the number of categories is fixed. However, DNNs notoriously fail when tasked with label prediction in a non-stationary data stream scenario, which has the continuous emergence of the unknown or novel class (categories not in the training set). For example, new topics continually emerge in social media or e-commerce. To solve this challenge, a DNN should not only be able to detect the novel class effectively but also incrementally learn new concepts from limited samples over time. Literature that addresses both problems simultaneously is limited. In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories. Different from existing approaches that rely on abundant labeled instances to re-train/update the model, we propose a new approach based on Few Sample and Adversarial Representation Learning (FSAR). The key novelty is that we introduce the adversarial confusion term into both the representation learning and few-sample learning process, which reduces the over-confidence of the model on the seen classes, further enhance the generalization of the model to detect and learn new categories with only a few samples. We train the FSAR operated in two stages: first, FSAR learns an intra-class compacted and inter-class separated feature embedding to detect the novel classes; next, we collect a few labeled samples belong to the new categories, utilize episode-training to exploit the intrinsic features for few-sample learning. We evaluated FSAR on different datasets, using extensive experimental results from various simulated stream benchmarks to show that FSAR effectively outperforms current state-of-the-art approaches.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88059010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang
Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.
{"title":"Multimodal Post Attentive Profiling for Influencer Marketing","authors":"Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang","doi":"10.1145/3366423.3380052","DOIUrl":"https://doi.org/10.1145/3366423.3380052","url":null,"abstract":"Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90042786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Tran, Mohamed H. Gad-Elrab, D. Stepanova, E. Kharlamov, Jannik Strotgen
Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.
{"title":"Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs","authors":"T. Tran, Mohamed H. Gad-Elrab, D. Stepanova, E. Kharlamov, Jannik Strotgen","doi":"10.1145/3366423.3380014","DOIUrl":"https://doi.org/10.1145/3366423.3380014","url":null,"abstract":"Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87684406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuan Deng, Sébastien Lahaie, V. Mirrokni, Song Zuo
An incentive-compatible auction incentivizes buyers to truthfully reveal their private valuations. However, many ad auction mechanisms deployed in practice are not incentive-compatible, such as first-price auctions (for display advertising) and the generalized second-price auction (for search advertising). We introduce a new metric to quantify incentive compatibility in both static and dynamic environments. Our metric is data-driven and can be computed directly through black-box auction simulations without relying on reference mechanisms or complex optimizations. We provide interpretable characterizations of our metric and prove that it is monotone in auction parameters for several mechanisms used in practice, such as soft floors and dynamic reserve prices. We empirically evaluate our metric on ad auction data from a major ad exchange and a major search engine to demonstrate its broad applicability in practice.
{"title":"A Data-Driven Metric of Incentive Compatibility","authors":"Yuan Deng, Sébastien Lahaie, V. Mirrokni, Song Zuo","doi":"10.1145/3366423.3380249","DOIUrl":"https://doi.org/10.1145/3366423.3380249","url":null,"abstract":"An incentive-compatible auction incentivizes buyers to truthfully reveal their private valuations. However, many ad auction mechanisms deployed in practice are not incentive-compatible, such as first-price auctions (for display advertising) and the generalized second-price auction (for search advertising). We introduce a new metric to quantify incentive compatibility in both static and dynamic environments. Our metric is data-driven and can be computed directly through black-box auction simulations without relying on reference mechanisms or complex optimizations. We provide interpretable characterizations of our metric and prove that it is monotone in auction parameters for several mechanisms used in practice, such as soft floors and dynamic reserve prices. We empirically evaluate our metric on ad auction data from a major ad exchange and a major search engine to demonstrate its broad applicability in practice.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76932737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Personalized search improves generic ranking models by taking user interests into consideration and returning more accurate search results to individual users. In recent years, machine learning and deep learning techniques have been successfully applied in personalized search. Most existing personalization models simply regard the search history as a static set of user behaviours and learn fixed ranking strategies based on the recorded data. Though improvements have been observed, it is obvious that these methods ignore the dynamic nature of the search process: search is a sequence of interactions between the search engine and the user. During the search process, the user interests may dynamically change. It would be more helpful if a personalized search model could track the whole interaction process and update its ranking strategy continuously. In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP). In RLPer, the search engine interacts with the user to update the underlying ranking model continuously with real-time feedback. And we design a feedback-aware personalized ranking component to catch the user’s feedback which has impacts on the user interest profile for the next query. Experimental results on the publicly available AOL search log verify that our proposed model can significantly outperform state-of-the-art personalized search models.
{"title":"RLPer: A Reinforcement Learning Model for Personalized Search","authors":"Jing Yao, Zhicheng Dou, Jun Xu, Ji-rong Wen","doi":"10.1145/3366423.3380294","DOIUrl":"https://doi.org/10.1145/3366423.3380294","url":null,"abstract":"Personalized search improves generic ranking models by taking user interests into consideration and returning more accurate search results to individual users. In recent years, machine learning and deep learning techniques have been successfully applied in personalized search. Most existing personalization models simply regard the search history as a static set of user behaviours and learn fixed ranking strategies based on the recorded data. Though improvements have been observed, it is obvious that these methods ignore the dynamic nature of the search process: search is a sequence of interactions between the search engine and the user. During the search process, the user interests may dynamically change. It would be more helpful if a personalized search model could track the whole interaction process and update its ranking strategy continuously. In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP). In RLPer, the search engine interacts with the user to update the underlying ranking model continuously with real-time feedback. And we design a feedback-aware personalized ranking component to catch the user’s feedback which has impacts on the user interest profile for the next query. Experimental results on the publicly available AOL search log verify that our proposed model can significantly outperform state-of-the-art personalized search models.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89915837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vasudevan Nagendra, A. Bhattacharya, V. Yegneswaran, Amir Rahmati, Samir R Das
Consumer IoT networks are characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and secure management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables intent-based conflict-free policy specification and enforcement in IoT environments. VISCR converts the topology of the IoT infrastructure into a tree-based abstraction and translates existing policies from heterogeneous vendor-specific programming languages, such as Groovy-based SmartThings, OpenHAB, IFTTT-based templates, and MUD-based profiles, into a vendor-independent graph-based specification. These are then used to automatically detect rogue policies, policy conflicts, and automation bugs. We evaluated VISCR using a dataset of 907 IoT apps, programmed using heterogeneous automation specifications, in a simulated smart-building IoT infrastructure. In our experiments, among 907 IoT apps, VISCR exposed 342 of IoT apps as exhibiting one or more violations, while also running 14.2x faster than the state-of-the-art tool (Soteria). VISCR detected 100% of violations reported by Soteria while also detecting new types of violations in 266 additional apps.
{"title":"An Intent-Based Automation Framework for Securing Dynamic Consumer IoT Infrastructures","authors":"Vasudevan Nagendra, A. Bhattacharya, V. Yegneswaran, Amir Rahmati, Samir R Das","doi":"10.1145/3366423.3380234","DOIUrl":"https://doi.org/10.1145/3366423.3380234","url":null,"abstract":"Consumer IoT networks are characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and secure management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables intent-based conflict-free policy specification and enforcement in IoT environments. VISCR converts the topology of the IoT infrastructure into a tree-based abstraction and translates existing policies from heterogeneous vendor-specific programming languages, such as Groovy-based SmartThings, OpenHAB, IFTTT-based templates, and MUD-based profiles, into a vendor-independent graph-based specification. These are then used to automatically detect rogue policies, policy conflicts, and automation bugs. We evaluated VISCR using a dataset of 907 IoT apps, programmed using heterogeneous automation specifications, in a simulated smart-building IoT infrastructure. In our experiments, among 907 IoT apps, VISCR exposed 342 of IoT apps as exhibiting one or more violations, while also running 14.2x faster than the state-of-the-art tool (Soteria). VISCR detected 100% of violations reported by Soteria while also detecting new types of violations in 266 additional apps.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84596450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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