Proceedings of the 9th ACM Conference on Recommender Systems最新文献_第3页

Personalized Catch-up & DVR: VOD or Linear, That is the Question 个性化追赶& DVR: VOD或线性，这是一个问题

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799493

Pancrazio Auteri, R. Turrin

{"title":"Personalized Catch-up & DVR: VOD or Linear, That is the Question","authors":"Pancrazio Auteri, R. Turrin","doi":"10.1145/2792838.2799493","DOIUrl":"https://doi.org/10.1145/2792838.2799493","url":null,"abstract":"The expansion of TV services such as DVR and, more recently, Catch-up have removed the temporal constraint typical of the Linear \"appointment\" TV enabling users to watch content they love at any time and on-demand. However, the DVR and Catch-up TV libraries, while providing a convenient time-shifted \"on-demand\" consumption, are indeed composed by content recently aired on a linear channel, so that they have more in common with Linear TV than they have with VOD. In this talk we will present and discuss the main challenges and some possible solutions to personalize the user experience with content from DVR and Catch-up TV, such as: (i) The consumption pattern is strongly affected by the context (e.g., time and device used to access the video service). (ii) Some content is consumed serially and still follows seasonal dynamics (e.g., TV Series). (iii) The system is fed with a massive and very dynamic streams of data (e.g., new content arriving right after broadcast, signals of user interactions). (iv) The same piece of content may coexist across multiple services provided by the same operator (e.g., linear schedule, network-DVR, catch-up TV, subscription VOD, rental VOD).","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130754821","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}

引用次数: 1

Data Quality Matters in Recommender Systems 推荐系统中的数据质量问题

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799670

Oren Sar Shalom, S. Berkovsky, Royi Ronen, Elad Ziklik, A. Amir

引用次数: 19

Context-aware Preference Modeling with Factorization 基于因子分解的上下文感知偏好建模

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796543

Balázs Hidasi

引用次数: 8

EMPIRE 2015: Workshop on Emotions and Personality in Personalized Systems 帝国2015:个性化系统中的情感和个性研讨会

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798716

M. Tkalcic, B. D. Carolis, M. Degemmis, Ante Odic, A. Košir

引用次数: 1

Making Meaningful Restaurant Recommendations At OpenTable 在OpenTable提供有意义的餐厅推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799501

Sudeep Das

引用次数: 6

Top-N Recommendation with Missing Implicit Feedback 缺少隐式反馈的Top-N推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799671

Daryl Lim, Julian McAuley, Gert R. G. Lanckriet

引用次数: 41

Preference-oriented Social Networks: Group Recommendation and Inference 偏好导向的社会网络:群体推荐与推理

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800190

Amirali Salehi-Abari, Craig Boutilier

引用次数: 62

Cold-Start Item and User Recommendation with Decoupled Completion and Transduction 解耦补全和转导的冷启动项目和用户推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800196

Iman Barjasteh, R. Forsati, Farzan Masrour, A. Esfahanian, H. Radha

{"title":"Cold-Start Item and User Recommendation with Decoupled Completion and Transduction","authors":"Iman Barjasteh, R. Forsati, Farzan Masrour, A. Esfahanian, H. Radha","doi":"10.1145/2792838.2800196","DOIUrl":"https://doi.org/10.1145/2792838.2800196","url":null,"abstract":"A major challenge in collaborative filtering based recommender systems is how to provide recommendations when rating data is sparse or entirely missing for a subset of users or items, commonly known as the cold-start problem. In recent years, there has been considerable interest in developing new solutions that address the cold-start problem. These solutions are mainly based on the idea of exploiting other sources of information to compensate for the lack of rating data. In this paper, we propose a novel algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, the proposed algorithm decouples the following two aspects of the cold-start problem: (a) the completion of a rating sub-matrix, which is generated by excluding cold-start users and items from the original rating matrix; and (b) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference significantly boosts the performance when appropriate side information is incorporated. We provide theoretical guarantees on the estimation error of the proposed two-stage algorithm based on the richness of similarity information in capturing the rating data. To the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees. We also conduct thorough experiments on synthetic and real datasets that demonstrate the effectiveness of the proposed algorithm and highlights the usefulness of auxiliary information in dealing with both cold-start users and items.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122506","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}

引用次数: 64

The Application of Recommender Systems in a Multi Site, Multi Domain Environment 推荐系统在多站点、多领域环境中的应用

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799495

Steven Bourke

{"title":"The Application of Recommender Systems in a Multi Site, Multi Domain Environment","authors":"Steven Bourke","doi":"10.1145/2792838.2799495","DOIUrl":"https://doi.org/10.1145/2792838.2799495","url":null,"abstract":"Recommender systems have cemented themselves in the daily experiences of most online users. In this work we will elaborate on the different challenges faced when creating recommendations in the following domains - Online marketplaces: Two sided marketplaces where buyers and sellers can interact and sell items with each other. - Online News: Online news sites where users consume the latest news articles related to current affairs. - Generic Recommendations: Sites which create generic recommendations based on generalised algorithms. We will review how we address these different challenges in Schibsted. Schibsted is an international media company with over 200 million unique users a month, split across 39 countries across the world. Concretely we will review, and compare the primary challenges between the different domains mentioned as well as the commonalities and general lessons we have learnt. For example in a two sided marketplace, it is important that both actors in the interaction are considered when creating recommendations. Constraints such as price sensitivity and geographical location become important when identifying good quality recommendations for our users. Alternatively, in online news we need to consider issues such as freshness and topical relevance when creating recommendations for users, while also striving to ensure we have editorial satisfaction. Finally we can look to generic recommendation solutions where we provide simple recommendation API end points. In this case it is important to ensure good quality recommendations while ensuring a generic enough solution that it can be used in many different scenarios. What makes these challenges particularly interesting is that we approach these different challenges with a holistic view of for improving the overall user experience for our users in Schibsted.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124312863","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}

引用次数: 2

ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations ExcUseMe:请求用户在项目冷启动建议中提供帮助

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800183

M. Aharon, Oren Anava, Noa Avigdor-Elgrabli, Dana Drachsler-Cohen, Shahar Golan, O. Somekh

{"title":"ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations","authors":"M. Aharon, Oren Anava, Noa Avigdor-Elgrabli, Dana Drachsler-Cohen, Shahar Golan, O. Somekh","doi":"10.1145/2792838.2800183","DOIUrl":"https://doi.org/10.1145/2792838.2800183","url":null,"abstract":"The item cold-start problem is of a great importance in collaborative filtering (CF) recommendation systems. It arises when new items are added to the inventory and the system cannot model them properly since it relies solely on historical users' interactions (e.g., ratings). Much work has been devoted to mitigate this problem mostly by employing hybrid approaches that combine content-based recommendation techniques or by devoting a portion of the user traffic for exploration to gather interactions from random users. We focus on pure CF recommender systems (i.e., without content or context information) in a realistic online setting, where random exploration is inefficient and smart exploration that carefully selects users is crucial due to the huge flux of new items with short lifespan. We further assume that users arrive randomly one after the other and that the system has to immediately decide whether the arriving user will participate in the exploration of the new items. For this setting we present ExcUseMe, a smart exploration algorithm that selects a predefined number of users for exploring new items. ExcUseMe gradually excavates the users that are more likely to be interested in the new items and models the new items based on the users' interactions. We evaluated ExcUseMe on several datasets and scenarios and compared it to state-of-the-art algorithms. Experimental results indicate that ExcUseMe is an efficient algorithm that outperforms all other algorithms in all tested scenarios.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125756046","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}

引用次数: 30