a hybrid approach to item recommendation in folksonomies Robert wetzke Winfried umbrath Alan Said DAI Labor DAI Labor DAI Labor Technische Universitat Berlin Technische Universitat Berlin Technische Universitat Berlin robert, wetzker@dai winfried. umbrath @dai id@dai-laborde labor. de labor de ⊥ BSTRACT tion in large folksonomies. Our intent is to help folksonomy In this paper we consider the problem of item recom users discover new interesting items based on their item his. mendation in collaborative tagging communities, so called tory. To this mean, we exploit the semantic contribution folksonomies. where annotate interesting items with of tags and extend the classical collaborative filtering ags. Rather than following a collaborative filtering or proach by user-generated annotations. This allows us to im- annotation-based approach to recommendation, we extend prove recommendations by calculating item similarities not the probabilistic latent semantic analysis(PLSA)approach only based on the user item distribution, but also in the tag and present a unified recommendation model which evolves space. Our approach is thus algorithmically related to previ- from item user and item tag co-occurrences in parallel. The ously presented work on hybrid recommender systems tha inclusion of tags reduces known collaborative filtering prob- combine collaborative and content-based models for better lems related to overfitting and allows for higher quality rec- recommendation quality. However, instead of relying on the ommendations. Experimental results on a large snapshot often complex, hard to extract and possibly heterogeneous of the delicious bookmarking service show the scalability content of items, we only consider an items annotations of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based The probabilistic latent semantic analysis(PLSA), as in troduced by Hofmann [9, has been shown to improve rec- ommendation quality for various settings by assuming a la Categories and Subject Descriptors tent lower dimensional topic model as origin of observed c H.3.3 Information Storage and Retrieval: Information occurrence distributions. Our approach extends the Plsa Search and Retrieval algorithm such that the topic model is estimated from the tem user as well as the item tag observations in parallel This allows us to benefit from user annotations during the Keywords recommender training and to combine collaborative and an folksonomies, tagging, recommendation, PLSA, delicious notation based models into a unified representation 1. INTRODUCTION The evaluation of our recommender system is performed or Collaborative tagging has become the most common con- large snapshot of 109 million bookmarks of the delicious tent categorization technique of the Web 2.0 age, allowing on-line bookmarking service The service allows its users the creators or consumers of content to assign freely chosen to centrally collect and manage their bookmarks by assign keywords(tags )in order to simplify later retrieval. The con- g tags. Being one of the first and most researched real pt of tagging has been proven successful in multiple areas world folksonomies, delicious represents a congruous eval- enabling the success of resource sharing services such as de uation object. Due to its large size, our dataset not only licious, last.fm or flickr. These social tagging communities reflects the structure and size of a real world social book- have become known as folksonomies. The distributed. user. marking corpus but also allows us to demonstrate the scala- centric annotation of (web-)content was shown to provide bility of our approach. Even though we limit our evaluation elevant meta-data and is expected to boost the semantic to social bookmarking, we believe our method is generally quality of labels applicable to the task of item recommendation in collabora- tive tagging communities. In this paper we consider the problem of item recommenda Historically, recommender systems are categorized into col- laborative filtering, content-based or hybrid systems, where the latter combine, or unify, user and content oriented a proaches and have shown to outperform their two-mode counterparts in many scenarios 3. Even though we do not consider the actual content of items rather item annotations generated by users, our scenario is algorithmically similar toA hybrid approach to item recommendation in folksonomies Robert Wetzker DAI Labor Technische Universität Berlin robert.wetzker@dai￾labor.de Winfried Umbrath DAI Labor Technische Universität Berlin winfried.umbrath@dai￾labor.de Alan Said DAI Labor Technische Universität Berlin alan.said@dai-labor.de ABSTRACT In this paper we consider the problem of item recom￾mendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering prob￾lems related to overfitting and allows for higher quality rec￾ommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval Keywords folksonomies, tagging, recommendation, PLSA, delicious 1. INTRODUCTION Collaborative tagging has become the most common con￾tent categorization technique of the Web 2.0 age, allowing the creators or consumers of content to assign freely chosen keywords (tags) in order to simplify later retrieval. The con￾cept of tagging has been proven successful in multiple areas, enabling the success of resource sharing services such as de￾licious, last.fm or flickr. These social tagging communities have become known as folksonomies. The distributed, user￾centric annotation of (web-) content was shown to provide relevant meta-data and is expected to boost the semantic quality of labels[7]. In this paper we consider the problem of item recommenda￾tion in large folksonomies. Our intent is to help folksonomy users discover new interesting items based on their item his￾tory. To this mean, we exploit the semantic contribution of tags and extend the classical collaborative filtering ap￾proach by user-generated annotations. This allows us to im￾prove recommendations by calculating item similarities not only based on the user item distribution, but also in the tag space. Our approach is thus algorithmically related to previ￾ously presented work on hybrid recommender systems that combine collaborative and content-based models for better recommendation quality. However, instead of relying on the often complex, hard to extract and possibly heterogeneous content of items, we only consider an item’s annotations. The probabilistic latent semantic analysis (PLSA), as in￾troduced by Hofmann [9], has been shown to improve rec￾ommendation quality for various settings by assuming a la￾tent lower dimensional topic model as origin of observed co￾occurrence distributions. Our approach extends the PLSA algorithm such that the topic model is estimated from the item user as well as the item tag observations in parallel. This allows us to benefit from user annotations during the recommender training and to combine collaborative and an￾notation based models into a unified representation. The evaluation of our recommender system is performed on a large snapshot of 109 million bookmarks of the delicious on-line bookmarking service1 . The service allows its users to centrally collect and manage their bookmarks by assign￾ing tags. Being one of the first and most researched real world folksonomies, delicious represents a congruous eval￾uation object. Due to its large size, our dataset not only reflects the structure and size of a real world social book￾marking corpus but also allows us to demonstrate the scala￾bility of our approach. Even though we limit our evaluation to social bookmarking, we believe our method is generally applicable to the task of item recommendation in collabora￾tive tagging communities. 1.1 Related Work Historically, recommender systems are categorized into col￾laborative filtering, content-based or hybrid systems, where the latter combine, or unify, user and content oriented ap￾proaches and have shown to outperform their two-mode counterparts in many scenarios [3]. Even though we do not consider the actual content of items rather item annotations generated by users, our scenario is algorithmically similar to 1http://delicious.com