Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.
{"title":"Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens","authors":"Raghav Pavan Karumur, Tien T. Nguyen, J. Konstan","doi":"10.1145/2959100.2959140","DOIUrl":"https://doi.org/10.1145/2959100.2959140","url":null,"abstract":"Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114879008","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}
The purpose of recommenders is often summarized as "help the users find relevant items", and the predominant operationalization of this goal has been to focus on the ability to numerically estimate the users' preferences for unseen items or to provide users with item lists ranked in accordance to the estimated preferences. This dominant, albeit narrow, view of the recommendation problem has been tremendously helpful in advancing research in different ways, e.g., through the establishment of standardized evaluation procedures and metrics. In reality, recommender systems can serve a variety of purposes from the point of view of both consumers and providers. Most of the purposes, however, are significantly underexplored, even though many of them are arguably more aligned with the real-world expectations for recommenders than our current predominant paradigm. Therefore, it is important to revisit our conceptualizations of the potential goals of recommenders and their operationalization as research problems. In this paper, we discuss a framework of recommendation goals and purposes and highlight possible future directions and challenges related to the operationalization of such alternative problem formulations.
{"title":"Recommendations with a Purpose","authors":"D. Jannach, G. Adomavicius","doi":"10.1145/2959100.2959186","DOIUrl":"https://doi.org/10.1145/2959100.2959186","url":null,"abstract":"The purpose of recommenders is often summarized as \"help the users find relevant items\", and the predominant operationalization of this goal has been to focus on the ability to numerically estimate the users' preferences for unseen items or to provide users with item lists ranked in accordance to the estimated preferences. This dominant, albeit narrow, view of the recommendation problem has been tremendously helpful in advancing research in different ways, e.g., through the establishment of standardized evaluation procedures and metrics. In reality, recommender systems can serve a variety of purposes from the point of view of both consumers and providers. Most of the purposes, however, are significantly underexplored, even though many of them are arguably more aligned with the real-world expectations for recommenders than our current predominant paradigm. Therefore, it is important to revisit our conceptualizations of the potential goals of recommenders and their operationalization as research problems. In this paper, we discuss a framework of recommendation goals and purposes and highlight possible future directions and challenges related to the operationalization of such alternative problem formulations.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127163315","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}
Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.
{"title":"Latent Factor Representations for Cold-Start Video Recommendation","authors":"S. Roy, Sharath Chandra Guntuku","doi":"10.1145/2959100.2959172","DOIUrl":"https://doi.org/10.1145/2959100.2959172","url":null,"abstract":"Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276127","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}
As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users' preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens -- an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).
当用户浏览推荐系统时，他们会系统地考虑或跳过显示的大部分内容。很明显，这些眼睛注视模式包含了与这些用户偏好有关的丰富信号。然而，由于大多数推荐系统无法获得眼动追踪数据，这些信号并没有被广泛地纳入个性化模型。在这项工作中，我们证明了在基于网格的推荐界面中，通过将易于收集的用户浏览数据与来自少数用户的眼动追踪数据相结合，可以预测凝视。我们的技术能够利用少量的眼动追踪数据来推断其他用户的凝视模式。我们在MovieLens(一个在线电影推荐系统)中评估我们的预测模型。我们的研究结果表明，与只使用浏览数据相比，结合来自少数用户的眼动追踪数据显着提高了准确性，即使眼动追踪用户与测试用户不同(例如，预测用户是否会关注某个项目的AUC=0.823 vs. 0.693)。我们还证明了隐马尔可夫模型(hmm)可以应用于这种情况;在预测固着概率和通过贝叶斯推理捕捉界面规律性方面，该模型优于线性模型(AUC=0.823 vs. 0.757)。
{"title":"Gaze Prediction for Recommender Systems","authors":"Qian Zhao, Shuo Chang, F. M. Harper, J. Konstan","doi":"10.1145/2959100.2959150","DOIUrl":"https://doi.org/10.1145/2959100.2959150","url":null,"abstract":"As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users' preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens -- an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126408841","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}
Jiawei Hu, Zhiqiang Zhang, Jian Liu, C. Shi, Philip S. Yu, Bai Wang
In recent years, there is a surge of research on recommender system to alleviate the information overload. Many recommendation techniques have been proposed and they have achieved great successes in many applications. However, the explanation of recommendation results is an important but seldom addressed problem. In this paper, we organize the objects and relations in a recommender system with a heterogeneous information network, which integrates more informations and contains rich semantics. Then we employ a semantic meta path based personalized recommendation model and design a recommender system with explanation, called RecExp. The RecExp system has two unique features. (1) Semantic recommendation. RecExp provides different recommendation models to comply with users' requirements through setting of meta paths. (2) Interpretive recommendation. Under a hybrid recommendation model, RecExp provides the explanations for the recommendation results.
{"title":"RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network","authors":"Jiawei Hu, Zhiqiang Zhang, Jian Liu, C. Shi, Philip S. Yu, Bai Wang","doi":"10.1145/2959100.2959112","DOIUrl":"https://doi.org/10.1145/2959100.2959112","url":null,"abstract":"In recent years, there is a surge of research on recommender system to alleviate the information overload. Many recommendation techniques have been proposed and they have achieved great successes in many applications. However, the explanation of recommendation results is an important but seldom addressed problem. In this paper, we organize the objects and relations in a recommender system with a heterogeneous information network, which integrates more informations and contains rich semantics. Then we employ a semantic meta path based personalized recommendation model and design a recommender system with explanation, called RecExp. The RecExp system has two unique features. (1) Semantic recommendation. RecExp provides different recommendation models to comply with users' requirements through setting of meta paths. (2) Interpretive recommendation. Under a hybrid recommendation model, RecExp provides the explanations for the recommendation results.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126397318","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}
Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users' better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users.
{"title":"Behaviorism is Not Enough: Better Recommendations through Listening to Users","authors":"Michael D. Ekstrand, M. Willemsen","doi":"10.1145/2959100.2959179","DOIUrl":"https://doi.org/10.1145/2959100.2959179","url":null,"abstract":"Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users' better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126482410","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}
When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of recommender systems in real-world applications. The competition provided many lessons, and many more have been learned since the Grand Prize was awarded in 2009. The use of recommender systems in industry has continued to grow driven by the availability of many kinds of user data and the continued interest for the area within the research community. In this paper, we will describe what we see as the past, present, and future of recommender systems from an industry perspective.
{"title":"Past, Present, and Future of Recommender Systems: An Industry Perspective","authors":"X. Amatriain, J. Basilico","doi":"10.1145/2959100.2959144","DOIUrl":"https://doi.org/10.1145/2959100.2959144","url":null,"abstract":"When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of recommender systems in real-world applications. The competition provided many lessons, and many more have been learned since the Grand Prize was awarded in 2009. The use of recommender systems in industry has continued to grow driven by the availability of many kinds of user data and the continued interest for the area within the research community. In this paper, we will describe what we see as the past, present, and future of recommender systems from an industry perspective.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371978","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}
Toine Bogers, M. Koolen, C. Musto, P. Lops, G. Semeraro
While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.
{"title":"Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)","authors":"Toine Bogers, M. Koolen, C. Musto, P. Lops, G. Semeraro","doi":"10.1145/2959100.2959200","DOIUrl":"https://doi.org/10.1145/2959100.2959200","url":null,"abstract":"While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"756 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132746241","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}
Kurt Jacobson, Vidhya Murali, Edward Newett, B. Whitman, Romain Yon
Spotify is the world's largest on-demand music streaming company, with over 75 million active listeners choosing what to listen to among tens of millions songs. Discovery and personalization is a key part of the experience and critical to the success of the creator and consumer ecosystem. In this talk, we'll discuss the state of our current discovery approaches, such as the Discover Weekly playlist that has already streamed billions of new discoveries and Fresh Finds, a scalable platform for brand new music that focuses suggestions on the long end of the popularity tail. We'll discuss the technologies at scale necessary to distill the information about music from our listeners and the world at large we collect outside of Spotify -- with the massive amounts of user-item activity data we collect every day to create highly personalized music experiences. Entire teams at Spotify focus on understanding both the creator and listener through collaborative filtering, machine learning, DSP and NLP approaches -- we crawl the web for artist information, scan each note in every one of our millions of songs for acoustic signals, and model users' taste through a cluster analysis and in a latent space based on their historical and real-time listening patterns. The data generated by these analyses have ensured our discovery products are precise and help our users enjoy music and media across our entire catalog. We'll dive deep into the workings of Discover Weekly, our marquee personalized playlist which updates weekly and reached 1 billion streams within the first 10 weeks from its release. The technology behind Discover Weekly is powered by a scalable factor analysis of Spotify's over two billion user-generated playlists matched to each user's current listening behavior. We'll discuss its innovative genesis and the challenges and opportunities the system faces a year after its launch. We'll also discuss Spotify's home page, seen by each of our users, currently undergoing vast efforts around personalization to ensure each listener gets a targeted list of playlists, shows and music to select throughout their day. We'll discuss the various similarity metrics, ranking approaches and user modeling we're working on to increase precision and optimize for our users' happiness.
{"title":"Music Personalization at Spotify","authors":"Kurt Jacobson, Vidhya Murali, Edward Newett, B. Whitman, Romain Yon","doi":"10.1145/2959100.2959120","DOIUrl":"https://doi.org/10.1145/2959100.2959120","url":null,"abstract":"Spotify is the world's largest on-demand music streaming company, with over 75 million active listeners choosing what to listen to among tens of millions songs. Discovery and personalization is a key part of the experience and critical to the success of the creator and consumer ecosystem. In this talk, we'll discuss the state of our current discovery approaches, such as the Discover Weekly playlist that has already streamed billions of new discoveries and Fresh Finds, a scalable platform for brand new music that focuses suggestions on the long end of the popularity tail. We'll discuss the technologies at scale necessary to distill the information about music from our listeners and the world at large we collect outside of Spotify -- with the massive amounts of user-item activity data we collect every day to create highly personalized music experiences. Entire teams at Spotify focus on understanding both the creator and listener through collaborative filtering, machine learning, DSP and NLP approaches -- we crawl the web for artist information, scan each note in every one of our millions of songs for acoustic signals, and model users' taste through a cluster analysis and in a latent space based on their historical and real-time listening patterns. The data generated by these analyses have ensured our discovery products are precise and help our users enjoy music and media across our entire catalog. We'll dive deep into the workings of Discover Weekly, our marquee personalized playlist which updates weekly and reached 1 billion streams within the first 10 weeks from its release. The technology behind Discover Weekly is powered by a scalable factor analysis of Spotify's over two billion user-generated playlists matched to each user's current listening behavior. We'll discuss its innovative genesis and the challenges and opportunities the system faces a year after its launch. We'll also discuss Spotify's home page, seen by each of our users, currently undergoing vast efforts around personalization to ensure each listener gets a targeted list of playlists, shows and music to select throughout their day. We'll discuss the various similarity metrics, ranking approaches and user modeling we're working on to increase precision and optimize for our users' happiness.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996384","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}
Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and integrates additional contextual factors, concretely, user's geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social influence.
{"title":"Discovering What You're Known For: A Contextual Poisson Factorization Approach","authors":"Haokai Lu, James Caverlee, Wei Niu","doi":"10.1145/2959100.2959146","DOIUrl":"https://doi.org/10.1145/2959100.2959146","url":null,"abstract":"Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and integrates additional contextual factors, concretely, user's geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social influence.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413607","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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