Jan Neumann, John Hannon, Claudio Riefolo, H. Sayyadi
For many households the television is the central entertainment hub in their home, and the average TV viewer spends about half of their leisure time in front of a TV. At any given moment, a costumer has hundreds to thousands of entertainment choices available, which makes some sort of automatic, personalized recommendations desirable to help consumers deal with the often overwhelming number of choices they face. The 3rd Workshop on Recommendation Systems for Television and Online Video aims to offer a place to present and discuss the latest academic and industrial research on recommendation systems for this challenging and exciting application domain.
{"title":"3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016)","authors":"Jan Neumann, John Hannon, Claudio Riefolo, H. Sayyadi","doi":"10.1145/2959100.2959198","DOIUrl":"https://doi.org/10.1145/2959100.2959198","url":null,"abstract":"For many households the television is the central entertainment hub in their home, and the average TV viewer spends about half of their leisure time in front of a TV. At any given moment, a costumer has hundreds to thousands of entertainment choices available, which makes some sort of automatic, personalized recommendations desirable to help consumers deal with the often overwhelming number of choices they face. The 3rd Workshop on Recommendation Systems for Television and Online Video aims to offer a place to present and discuss the latest academic and industrial research on recommendation systems for this challenging and exciting application domain.","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":"130851246","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}
Denise Ichinco, Sahil Zubair, J. Eggers, N. Wilson
At Nara Logics, we provide recommendations for ecommerce, supply chain, financial services, travel & hospitality, operations and more for the Global 200. We've learned that for machine intelligence to be accepted, it must interact seamlessly with humans, expose its reasoning to humans, and even incorporate human feedback in real time into its decision making. Just as you take your friends' recommendations more seriously when you can probe their mental model of your likes and dislikes, machine recommendations are more appealing when users understand how they were generated and can provide feedback to those recommendations. These aspects are necessary as commercial interfaces increasingly leverage recommendations alongside statistical analysis.
{"title":"A Cross-Industry Machine Learning Framework with Explicit Representations","authors":"Denise Ichinco, Sahil Zubair, J. Eggers, N. Wilson","doi":"10.1145/2959100.2959125","DOIUrl":"https://doi.org/10.1145/2959100.2959125","url":null,"abstract":"At Nara Logics, we provide recommendations for ecommerce, supply chain, financial services, travel & hospitality, operations and more for the Global 200. We've learned that for machine intelligence to be accepted, it must interact seamlessly with humans, expose its reasoning to humans, and even incorporate human feedback in real time into its decision making. Just as you take your friends' recommendations more seriously when you can probe their mental model of your likes and dislikes, machine recommendations are more appealing when users understand how they were generated and can provide feedback to those recommendations. These aspects are necessary as commercial interfaces increasingly leverage recommendations alongside statistical analysis.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"12 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":"134508421","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}
Repeat Purchases have become increasingly important in measuring customer's satisfaction and loyalty to e-commerce websites in regard to online shopping. In this paper, we first propose a model for estimating repeat purchase frequency in a given time period from a given product category using Poisson/Gamma model. Second, we estimate the purchase probabilities of different product types in a product category for each customer using Dirichlet model. Experimental results on data collected by a real-world e-commerce website show that it can predict a user's average repeat purchase frequency along with their product types with decent accuracy. We also argue that the output of our models can be used as prior information to enhance the performance of time-sensitive recommendation.
{"title":"Recommending Repeat Purchases using Product Segment Statistics","authors":"Suvodip Dey, Pabitra Mitra, K. Gupta","doi":"10.1145/2959100.2959145","DOIUrl":"https://doi.org/10.1145/2959100.2959145","url":null,"abstract":"Repeat Purchases have become increasingly important in measuring customer's satisfaction and loyalty to e-commerce websites in regard to online shopping. In this paper, we first propose a model for estimating repeat purchase frequency in a given time period from a given product category using Poisson/Gamma model. Second, we estimate the purchase probabilities of different product types in a product category for each customer using Dirichlet model. Experimental results on data collected by a real-world e-commerce website show that it can predict a user's average repeat purchase frequency along with their product types with decent accuracy. We also argue that the output of our models can be used as prior information to enhance the performance of time-sensitive recommendation.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"2016 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":"132640161","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}
Ido Tamir, Royce Bass, Guy Kobrinsky, Baruch Brutman, R. Lempel, Yoram Dayagi
Outbrain is the Web's leading content discovery service, recommending billions of stories daily to a global audience across many of the world's most prestigious and respected publishers. Outbrain's recommendation technology com- bines contextual cues with personalization, where the per- sonalization aspects are a combination of content-based and collaborative filtering techniques. This paper, and the accompanying demo, offer a behind- the-scenes view of the content-based aspects of Outbrain's personalization technology. We detail the types of features we extract from content, as well as the attributes we keep in each user's content-affinity profile. We then describe and demonstrate how we update each user's profile, in real time, as the user consumes content while browsing the Web.
{"title":"Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences","authors":"Ido Tamir, Royce Bass, Guy Kobrinsky, Baruch Brutman, R. Lempel, Yoram Dayagi","doi":"10.1145/2959100.2959111","DOIUrl":"https://doi.org/10.1145/2959100.2959111","url":null,"abstract":"Outbrain is the Web's leading content discovery service, recommending billions of stories daily to a global audience across many of the world's most prestigious and respected publishers. Outbrain's recommendation technology com- bines contextual cues with personalization, where the per- sonalization aspects are a combination of content-based and collaborative filtering techniques. This paper, and the accompanying demo, offer a behind- the-scenes view of the content-based aspects of Outbrain's personalization technology. We detail the types of features we extract from content, as well as the attributes we keep in each user's content-affinity profile. We then describe and demonstrate how we update each user's profile, in real time, as the user consumes content while browsing the Web.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"6 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":"133944192","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}
Amra Delic, J. Neidhardt, T. Nguyen, F. Ricci, L. Rook, H. Werthner, M. Zanker
Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.
{"title":"Observing Group Decision Making Processes","authors":"Amra Delic, J. Neidhardt, T. Nguyen, F. Ricci, L. Rook, H. Werthner, M. Zanker","doi":"10.1145/2959100.2959168","DOIUrl":"https://doi.org/10.1145/2959100.2959168","url":null,"abstract":"Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"70 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":"121029423","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}
C. Musto, F. Narducci, P. Lops, M. Degemmis, G. Semeraro
In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through the properties available in the LOD cloud. Next, given this graph, we implemented some techniques to rank those properties and we used the most relevant ones to feed a module for generating explanations in natural language. In the experimental evaluation we performed a user study with 308 subjects aiming to investigate to what extent our explanation framework can lead to more transparent, trustful and engaging recommendations. The preliminary results provided us with encouraging findings, since our algorithm performed better than both a non-personalized explanation baseline and a popularity-based one.
{"title":"ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud","authors":"C. Musto, F. Narducci, P. Lops, M. Degemmis, G. Semeraro","doi":"10.1145/2959100.2959173","DOIUrl":"https://doi.org/10.1145/2959100.2959173","url":null,"abstract":"In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through the properties available in the LOD cloud. Next, given this graph, we implemented some techniques to rank those properties and we used the most relevant ones to feed a module for generating explanations in natural language. In the experimental evaluation we performed a user study with 308 subjects aiming to investigate to what extent our explanation framework can lead to more transparent, trustful and engaging recommendations. The preliminary results provided us with encouraging findings, since our algorithm performed better than both a non-personalized explanation baseline and a popularity-based one.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"20 5 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":"128916185","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}
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.
{"title":"Ask the GRU: Multi-task Learning for Deep Text Recommendations","authors":"Trapit Bansal, David Belanger, A. McCallum","doi":"10.1145/2959100.2959180","DOIUrl":"https://doi.org/10.1145/2959100.2959180","url":null,"abstract":"In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"63 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":"128683748","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}
People recommenders have become a rich research area within the broad recommender systems community and social recommender systems in particular. From "people you may know" and "who to follow" widgets, through people introduction at conferences, job recommendations and job-candidate search, to dating partner matchmakers, people recommendations proliferate. This tutorial will present an overview of the people recommender systems domain. We will present the different types and use cases of people recommendations, the special techniques used to recommend people to themselves, key research work, and open challenges.
{"title":"People Recommendation Tutorial","authors":"Ido Guy, L. Pizzato","doi":"10.1145/2959100.2959196","DOIUrl":"https://doi.org/10.1145/2959100.2959196","url":null,"abstract":"People recommenders have become a rich research area within the broad recommender systems community and social recommender systems in particular. From \"people you may know\" and \"who to follow\" widgets, through people introduction at conferences, job recommendations and job-candidate search, to dating partner matchmakers, people recommendations proliferate. This tutorial will present an overview of the people recommender systems domain. We will present the different types and use cases of people recommendations, the special techniques used to recommend people to themselves, key research work, and open challenges.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"13 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":"123674584","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}
Classical recommender systems provide users with ranked lists of recommendations, where each one consists of a single item. However, these ranked lists are not suitable for applications such as trip planning, which deal with heterogeneous items. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of different Points of Interest that may constitute a tour. Given a collection of POIs, our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. Experimental evaluation of our proposed system, using a real-world dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.
{"title":"A Package Recommendation Framework for Trip Planning Activities","authors":"Idir Benouaret, D. Lenne","doi":"10.1145/2959100.2959183","DOIUrl":"https://doi.org/10.1145/2959100.2959183","url":null,"abstract":"Classical recommender systems provide users with ranked lists of recommendations, where each one consists of a single item. However, these ranked lists are not suitable for applications such as trip planning, which deal with heterogeneous items. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of different Points of Interest that may constitute a tour. Given a collection of POIs, our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. Experimental evaluation of our proposed system, using a real-world dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"29 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":"125039890","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}
C. Teo, Houssam Nassif, Daniel N. Hill, S. Srinivasan, Mitchell Goodman, Vijai Mohan, S. Vishwanathan
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we explore extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon. Our system presents the user with a diverse set of interesting items while adapting to user interactions. Our solution consists of three components (1) a Bayesian regression model for scoring the relevance of items while leveraging uncertainty, (2) a submodular diversification framework that re-ranks the top scoring items based on category, and (3) personalized category preferences learned from the user's behavior. When tested on live traffic, our algorithms show a strong lift in click-through-rate and session duration.
{"title":"Adaptive, Personalized Diversity for Visual Discovery","authors":"C. Teo, Houssam Nassif, Daniel N. Hill, S. Srinivasan, Mitchell Goodman, Vijai Mohan, S. Vishwanathan","doi":"10.1145/2959100.2959171","DOIUrl":"https://doi.org/10.1145/2959100.2959171","url":null,"abstract":"Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we explore extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon. Our system presents the user with a diverse set of interesting items while adapting to user interactions. Our solution consists of three components (1) a Bayesian regression model for scoring the relevance of items while leveraging uncertainty, (2) a submodular diversification framework that re-ranks the top scoring items based on category, and (3) personalized category preferences learned from the user's behavior. When tested on live traffic, our algorithms show a strong lift in click-through-rate and session duration.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"60 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":"127848453","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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