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223
A matrix factorization technique with trust propagation for recommendation in social networks
- In RecSys
, 2010
"... Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the ad-vent of online social networks, the so ..."
Abstract
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Cited by 95 (3 self)
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Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the ad-vent of online social networks
Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering
"... Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user’s profiles unbound from a rigid predefined concept ..."
Abstract
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Cited by 99 (2 self)
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Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user’s profiles unbound from a rigid predefined
Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks
"... Location-based social networks (LBSNs) have attracted an inordinate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social information in online LBSNs offers an unprecedented opportunity to study various aspects of human behavior, ..."
Abstract
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Cited by 21 (5 self)
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, and enable a variety of location-based services such as location recommendation. Previous work studied spatial and social influences on location recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, recommender systems designed
A Research Platform for Recommendation within Social Networks
"... Recommendations within a network do affect, and get affected by, the information flow and the social connections within the network. Thus, designing a network-centric recommender system requires understanding people’s preferences, their social connections, as well as the characteristics of the netwo ..."
Abstract
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Recommendations within a network do affect, and get affected by, the information flow and the social connections within the network. Thus, designing a network-centric recommender system requires understanding people’s preferences, their social connections, as well as the characteristics
Towards Trust-aware Recommendations In Social Networks
"... for inspiring the first ideas for this thesis after wondering, upset, why last.fm hadn’t recommend him that upcoming concert of his favorite and most listened band, Los Planetas; for pointing me to Trust-aware Recommender Systems when I was lost, and for introducing me the Recommender Systems Handbo ..."
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researched within the last decade. With the emergence and popularization of social networks a new field has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account
PopCore: A system for Network-Centric Recommendations
"... In this paper we explore the idea of network-centric recommendations. In contrast to individually-oriented recommendations enabled by social network data, a network-centric approach to recommendations introduces new goals such as effective information exchange, enabling shared experiences, and suppo ..."
Abstract
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Cited by 1 (1 self)
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In this paper we explore the idea of network-centric recommendations. In contrast to individually-oriented recommendations enabled by social network data, a network-centric approach to recommendations introduces new goals such as effective information exchange, enabling shared experiences
Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
, 2008
"... This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popula ..."
Abstract
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Cited by 49 (1 self)
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popular products. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path dependent effects. Second, turning to simulation, we increase the realism of our results by combining
Dorian: Music Recommendation Strategies using Social Network Mining
, 2008
"... this work available for noncommercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copying is by permission of the author. To disseminate otherwise or to republish requires One of the many consequences of the developme ..."
Abstract
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of the development of technology and the free exchange of information is the abundance of easily accessible music. A multitude of music recommendation systems exist to guide the user in their exploration of music. Many of these systems learn the preferences of their users, but the recommendations they provide
Collaborative Filtering for People to People Recommendation
- in Social Networks,” in AI 2010: Advances in Artifical Intelligence
, 2010
"... Abstract. Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have ..."
Abstract
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Cited by 20 (5 self)
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Abstract. Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have
Results 1 - 10
of
223
