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Video recommendation over multiple information sources

Xiaojian Zhao, Jin Yuan, Meng Wang, Guangda Li, Richang Hong, Zhoujun Li, Tat-Seng Chua
2012 Multimedia Systems  
In particular, one novel source user's relationship strength is inferred through the online social network and applied to recommend videos.  ...  Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation.  ...  For example, Jebrin and Williams [26] proposed a new approach to make recommendations based on leaders' credibility in ''Follow the Leader'' model as Top-N recommender by incorporating social network  ... 
doi:10.1007/s00530-012-0267-z fatcat:vu2jxkwobjcshf3evbf4pxwxle

Collaborative filtering in social tagging systems based on joint item-tag recommendations

Jing Peng, Daniel Dajun Zeng, Huimin Zhao, Fei-yue Wang
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
In this framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items.  ...  In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items.  ...  We start with a review of the literature on collaborative filtering, as well as tag and item recommendations in social tagging systems.  ... 
doi:10.1145/1871437.1871541 dblp:conf/cikm/PengZZW10 fatcat:aw2txylqxvb6hln2fofqdj2rdy

BRS cS: a hybrid recommendation model fusing multi-source heterogeneous data

Zhenyan Ji, Chun Yang, Huihui Wang, José Enrique Armendáriz-iñigo, Marta Arce-Urriza
2020 EURASIP Journal on Wireless Communications and Networking  
It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations.  ...  User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating  ...  A user's top-N recommendation lisbobtained from (12) in descending order.  ... 
doi:10.1186/s13638-020-01716-2 fatcat:xqjvxo25wrbptpvx2rdsnsnfze

Learning Discriminative Recommendation Systems with Side Information

Feipeng Zhao, Yuhong Guo
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Top-N recommendation systems are useful in many real world applications such as E-commerce platforms.  ...  In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation  ...  Acknowledgments This research was supported in part by the Canada Research Chairs program. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2017/485 dblp:conf/ijcai/ZhaoG17 fatcat:y33z4sq72rcjha36g3g4hdarii

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Liang Zhang, Quanshen Wei, Lei Zhang, Baojiao Wang, Wen-Hsien Ho
2020 Applied Sciences  
In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user.  ...  A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining  ...  Rating Prediction and Top-N Re-Ranking Rating Prediction After ranking the aggregated weights, the top n users with the highest weights are selected as neighbors who participate in the rating prediction  ... 
doi:10.3390/app10041257 fatcat:55kvv3sgrzf25gehhlivewny3m

ACRec

Jing Li, Feng Xia, Wei Wang, Zhen Chen, Nana Yaw Asabere, Huizhen Jiang
2014 Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion  
In this paper, we satisfy the demand of collaboration recommendation through co-authorship in an academic network.  ...  On the other hand, discovering new collaborators who are smart enough to conduct joint-research work is accompanied with both difficulties and opportunities.  ...  Now we can recommend nodes in the TOP N of the list MR to target nodes. Of course, we can take the nodes out from the TOP N list, which have been in its co-author list before recommending.  ... 
doi:10.1145/2567948.2579034 dblp:conf/www/LiXWCAJ14 fatcat:3sqhiubepndzrk4rcwk6oehq2m

Multirelational Social Recommendations via Multigraph Ranking

Mingsong Mao, Jie Lu, Guangquan Zhang, Jinlong Zhang
2017 IEEE Transactions on Cybernetics  
With the users’ trust relationships in social networks to enhance neighbourhood of a given user established, the possible rating collaborative filtering.  ...  Liu, “On top-k recommendations using trust in web-based social networks,” in recommendation using social networks,” in The 6th ACM Conference Proceedings of the IEEE Consumer Communications  ... 
doi:10.1109/tcyb.2016.2595620 pmid:28113690 fatcat:qt6naqquwrbixoaqoi5gsegu6m

A local social network approach for research management

Xiaoyan Liu, Zhiling Guo, Zhenjiang Lin, Jian Ma
2013 Decision Support Systems  
In order to address the organizational research management needs, we propose a research social network approach to better analyze local collaboration networks.  ...  Insights derived from this research are very helpful for managers to effectively allocate resources, identify research priorities, promote collaboration, and grow research in directions aligned with the  ...  This is in contrast with researcher 24 who collaborated with others closely in local area (with degree of 8 and is ranked at 2nd), but has a weaker relationship with colleagues (closeness measure is ranked  ... 
doi:10.1016/j.dss.2012.10.055 fatcat:yullfrnyofgnnkytouu26n4rsa

Point of Interest Recommendation engine

T. SAKTHISREE, S. SIVASANKARI, S.GOKUL RAJ, S. ABIRAMI
2020 International Journal of Recent Trends in Engineering and Research  
In the second phase, our algorithm attempts to rank the preferred POI users higher on the recommendation list.  ...  While recent work has explored the thought of adopting a collaborative ranking (CR) for recommendations, few attempts are made to include time-based information for POI recommendations using CR.  ...  Collaborative Ranking (CR) is based on this concept and focuses on recommendations being correct at the top of the recommendation list for users each.  ... 
doi:10.23883/ijrter.2020.6013.5onhy fatcat:kobcu2nesbgwjftzmd57zeukse

Recommendation with Multi-Source Heterogeneous Information

Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
However, existing item recommendation models in social networks suffer from two limitations.  ...  Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation.  ...  Introduction With the massive amount of data generated by online social services, recommender systems are playing an important role in connecting users and information resources.  ... 
doi:10.24963/ijcai.2018/469 dblp:conf/ijcai/GaoYWZLH18 fatcat:63fvrois7ffsfk2ojwp3yeymly

Supporting novel biomedical research via multilayer collaboration networks

Konstantin Kuzmin, Xiaoyan Lu, Partha Sarathi Mukherjee, Juntao Zhuang, Chris Gaiteri, Boleslaw K. Szymanski
2016 Applied Network Science  
Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications.  ...  Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging and it must be supported in competition with established research programs.  ...  Ranks of the collaborators in top 120 recommendations produced by PageRank and the publication count method.  ... 
doi:10.1007/s41109-016-0015-y pmid:30533503 pmcid:PMC6245218 fatcat:xif4avvc2nbq3kc6hycrr5jtte

A Survey of Point-of-interest Recommendation in Location-based Social Networks [article]

Shenglin Zhao, Irwin King, Michael R. Lyu
2016 arXiv   pre-print
Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs).  ...  First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications.  ...  Then, the POI recommendation task could be achieved through ranking the candidate POIs and selecting the top N POIs with the highest estimated possibility values for each user.  ... 
arXiv:1607.00647v1 fatcat:prstldhamremzpkmbyf5oh6jom

ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval [article]

Lu Yu and Junming Huang and Chuang Liu and Zike Zhang
2014 arXiv   pre-print
By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given  ...  Interactions between search and recommendation have recently attracted significant attention, and several studies have shown that many potential applications involve with a joint problem of producing recommendations  ...  collaborative tagging behaviours, listening preferences on artists, as well as social relationship.  ... 
arXiv:1412.3898v1 fatcat:votlq6uw7nbdflsrwoquicojwi

Personalized Book Recommendations Created by Using Social Media Data [chapter]

Maria Soledad Pera, Nicole Condie, Yiu-Kai Ng
2011 Lecture Notes in Computer Science  
Even though existing book recommenders, which are based on either collaborative filtering, text content, or the hybrid approach, aid users in locating books (among the millions available), their recommendations  ...  a user's friends who share common interests with the user, in addition to applying word-correlation factors for partially matching book tags to disclose books similar in contents.  ...  Rank(CB) = Resem(Source Book, CB)×Close(Source Book, LT F riend) (4) The Top-N (N ≥ 1) books with the highest ranking score are recommended to LT U ser.  ... 
doi:10.1007/978-3-642-24396-7_31 fatcat:6rlwqoo4kbe5lf6rkl5tietzzq

Affiliation recommendation using auxiliary networks

Vishvas Vasuki, Nagarajan Natarajan, Zhengdong Lu, Inderjit S. Dhillon
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
In many social networks, besides friendship links amongst users, the phenomenon of users associating themselves with groups or communities is common.  ...  Social network analysis has attracted increasing attention in recent years.  ...  The relationship between the increase in sensitivity, as n increases, with the decrease in specificity is of interest in comparing the quality of recommendations.  ... 
doi:10.1145/1864708.1864731 dblp:conf/recsys/VasukiNLD10 fatcat:fartvctggfdb5ng74bysqseg7q
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