Matches in ScholarlyData for { ?s ?p ?o. }
- 258 keyword "relevance".
- 258 keyword "retrieval".
- 258 title "Diversifying Web Search Results".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent davood-rafiei.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent krishna-bharat.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent anand-shukla.
- item-3 previous item-2.
- 267 creator christoph-freudenthaler.
- 267 creator lars-schmidt-thieme.
- 267 creator steffen-rendle.
- 267 type InProceedings.
- 267 label "Factorizing Personalized Markov Chains for Next-Basket Recommendation".
- 267 sameAs 267.
- 267 abstract "Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned -- thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.".
- 267 hasAuthorList authorList.
- 267 isPartOf proceedings.
- 267 keyword "Personalization".
- 267 keyword "recommendation systems".
- 267 title "Factorizing Personalized Markov Chains for Next-Basket Recommendation".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent steffen-rendle.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent christoph-freudenthaler.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent lars-schmidt-thieme.
- item-3 previous item-2.
- 268 creator alon-halevy.
- 268 creator eldar-sadikov.
- 268 creator jayant-madhavan.
- 268 creator lu-wang.
- 268 type InProceedings.
- 268 label "Clustering Query Refinements by User Intent".
- 268 sameAs 268.
- 268 abstract "We address the problem of clustering the refinements of a user search query. The clusters computed by our proposed algorithm can be used to improve the selection and placement of the query suggestions proposed by a search engine, and can also serve to summarize the different aspects of information relevant to the original user query. Our algorithm clusters refinements based on their likely underlying user intents by combining document click and session co-occurrence information. At its core, our algorithm operates by performing multiple random walks on a Markov graph that approximates user search behavior. A user study performed on top search engine queries shows that our clusters are rated better than corresponding clusters computed using approaches that use only document click or only sessions co-occurrence information.".
- 268 hasAuthorList authorList.
- 268 isPartOf proceedings.
- 268 keyword "User models".
- 268 keyword "click logs".
- 268 keyword "personalization".
- 268 keyword "query logs".
- 268 title "Clustering Query Refinements by User Intent".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent eldar-sadikov.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent jayant-madhavan.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent lu-wang.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent alon-halevy.
- item-4 previous item-3.
- 283 creator can-wang.
- 283 creator chun-chen.
- 283 creator deng-cai.
- 283 creator jiajun-bu.
- 283 creator kun-yang.
- 283 creator ziyu-guan.
- 283 type InProceedings.
- 283 label "Document Recommendation in Social Tagging Services".
- 283 sameAs 283.
- 283 abstract "Social tagging services allow users to annotate various online resources with freely chosen keywords (tags). They not only facilitate the users in finding and organizing online resources, but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems. Traditional studies on recommender systems focused on user rating data, while recently social tagging data is becoming more and more prevalent. How to perform resource recommendation based on tagging data is an emerging research topic. In this paper we consider the problem of document (e.g. Web pages, research papers) recommendation using purely tagging data. That is, we only have data containing users, tags, documents and the relationships among them. We propose a novel graph-based representation learning algorithm for this purpose. The users, tags and documents are represented in the same semantic space in which two related objects are close to each other. For a given user, we recommend those documents that are sufficiently close to him/her. Experimental results on two data sets crawled from Del.icio.us and CiteULike show that our algorithm can generate promising recommendations and outperform traditional recommendation algorithms.".
- 283 hasAuthorList authorList.
- 283 isPartOf proceedings.
- 283 keyword "Personalization".
- 283 keyword "recommendation systems".
- 283 title "Document Recommendation in Social Tagging Services".
- authorList type List.
- authorList hasFirstItem item-1.