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874
Improving Sales Diversity by Recommending Users to Items
"... Sales diversity is considered a key feature of Recommender Systems from a business perspective. Sales diversity is also linked with the long-tail novelty of recommendations, a qual-ity dimension from the user perspective. We explore the inversion of the recommendation task as a means to en-hance sal ..."
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Cited by 1 (0 self)
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-hance sales diversity – and indirectly novelty – by selecting which users an item should be recommended to instead of the other way around. We address the inverted task by two approaches: a) inverting the rating matrix, and b) defining a probabilistic reformulation which isolates the popularity component
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1490 (23 self)
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various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items
Content-Boosted Collaborative Filtering for Improved Recommendations
- in Eighteenth National Conference on Artificial Intelligence
, 2002
"... Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid rec ..."
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Cited by 326 (3 self)
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Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid
Improving recommendation lists through topic diversification
, 2005
"... In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user’s complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recom ..."
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Cited by 293 (13 self)
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In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user’s complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction
Content-Based Book Recommending Using Learning for Text Categorization
- IN PROCEEDINGS OF THE FIFTH ACM CONFERENCE ON DIGITAL LIBRARIES
, 1999
"... Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. ..."
Abstract
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Cited by 334 (8 self)
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Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences
Improving Recommendation Diversity
, 2001
"... Recommender systems o#er users a more intelligent and personalised mechanism to seek out new information. Content-based recommender systems generally prefer to retrieve a set of items maximally similar to a users' query and/or profile. We argue that as new types of recommendation domains and ta ..."
Abstract
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Cited by 35 (1 self)
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Recommender systems o#er users a more intelligent and personalised mechanism to seek out new information. Content-based recommender systems generally prefer to retrieve a set of items maximally similar to a users' query and/or profile. We argue that as new types of recommendation domains
Content-based recommendation systems
- THE ADAPTIVE WEB: METHODS AND STRATEGIES OF WEB PERSONALIZATION. VOLUME 4321 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2007
"... This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news ..."
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Cited by 163 (0 self)
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, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types
Exploiting the Diversity of User Preferences for Recommendation
"... Diversity as a quality dimension for Recommender Systems has been receiving increasing attention in the last few years. This has been paralleled by an intense strand of research on diversity in search tasks, and in fact converging views on diversity theories and techniques from Information Retrieval ..."
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Cited by 3 (0 self)
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Retrieval and Recommender Systems have been put forward in recent work. In this paper we research diversity not only as a target property for a recommender system, but as an element in the input data, within and between user behaviors, that a recommender system can leverage to enhance the quality of its
Towards more diverse recommendations: Item re-ranking methods for recommender systems
- In Workshop on Information Technologies and Systems
, 2009
"... Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent ..."
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Cited by 11 (1 self)
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Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce a number of item re-ranking methods that can generate substantially more diverse recommendations across all users while
EFFECTS OF THE PLACEMENT OF DIVERSE ITEMS IN RECOMMENDATION LISTS
"... Abstract: Over the last fifteen years, a large amount of research in recommender systems was devoted to the development of algorithms that focus on improving the accuracy of recommendations. More recently, it has been proposed that accuracy is not the only factor that contributes to the quality of r ..."
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Cited by 1 (0 self)
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the diverse items dispersedly or together at the bottom of the list can increase the perceived diversity. In addition, we found that in the movie domain including diverse items in the recommendation list does not hurt user satisfaction, which means that recommender systems providers have some flexibility
Results 1 - 10
of
874
