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A Contextual Item-Based Collaborative Filtering Technology

Xueqing Tan, Pan Pan
2012 Intelligent Information Management  
information who are familiar with user's current context information in order to predict that which items will be preferred by user in his or her current context.  ...  In the process of the recommendation, user's important mobile contextual information are taken into account, and the technology combines with those ratings on the items in the users' historical contextual  ...  Introduction Traditional personalized recommendation systems are used to suggest products or information or service to the customers in some E-commerce sites.  ... 
doi:10.4236/iim.2012.43013 fatcat:gejbv56jurd2hheuln6mkwizd4

Tag-Driven Online Novel Recommendation with Collaborative Item Modeling

2018 Information  
Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores  ...  To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM) is proposed for online novel recommendation.  ...  Data sparsity and recommendation personalization are considered in an item-based approach, which is more effective than a user-based approach for online novel recommendation. 2.  ... 
doi:10.3390/info9040077 fatcat:syt5zj4ksncatekupn6saanm74

Regret Guarantees for Item-Item Collaborative Filtering [article]

Guy Bresler, Devavrat Shah, Luis F. Voloch
2016 arXiv   pre-print
The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row.  ...  We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering.  ...  Introduction A natural approach to automated recommendation systems is to use content specific data: similar words in two books' titles suggest that they are similar, and similarly for user's age and geographic  ... 
arXiv:1507.05371v2 fatcat:oco5c6lyofhjngi2ymjaf4m7jq

Improving collaborative recommendation based on item weight link prediction

2021 Turkish Journal of Electrical Engineering and Computer Sciences  
Filtering system also called recommender systems 4 are widely used to recommend items to users by similarity process such as Amazon, MovieLens, Cdnow, etc.  ...  In the 5 literature, to predict a link in a bipartite network, most methods are based either on a binary history (like, dislike) or 6 on the common neighbourhood of the active user.  ...  The recommendation system is based on this 5 weight to recommend or reject an item to a user. 10 R(i, j) represents the weight of the link (i, j) .  ... 
doi:10.3906/elk-2008-26 fatcat:mn6nmt5sejbxjonyfnbatyffd4

NAIS: Neural Attentive Item Similarity Model for Recommendation

Xiangnan He, Zhenkui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, Tat-Seng Chua
2018 IEEE Transactions on Knowledge and Data Engineering  
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization  ...  In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF.  ...  However, such heuristic-based approaches for estimating item similarities lack optimization tailored for recommendation, and thus may yield suboptimal performance.  ... 
doi:10.1109/tkde.2018.2831682 fatcat:te64o6xycfdenbnjqa7kbfzp3q

FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms [article]

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
2022 arXiv   pre-print
These recommendations not only help users compare items related to a given item, but also play a major role in bringing traffic to individual items, thus deciding the exposure that different items receive  ...  control on the exposure distribution, often at a small or no cost in terms of recommendation quality, measured in terms of relatedness and user satisfaction.  ...  This research is supported in part by (1) a grant from the Max Planck Society through a Max Planck Partner Group at IIT Kharagpur, and (2) a European Research Council (ERC) Advanced Grant for the project  ... 
arXiv:2204.00241v1 fatcat:ncnqwhesdreizicsp7pvwyaafa

Item Silk Road

Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped.  ...  Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs.  ...  Acknowledgement We would like to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1145/3077136.3080771 dblp:conf/sigir/Wang0NC17 fatcat:kqipy2xjezaq3j7mr2gms6dani

An Expected Item Bias based Hybrid Approach for Recommendation System

Kaikuo Xu, Changan Yuan, Fan Li, Xianbin Liu
2014 International Journal of Database Theory and Application  
To improve the accuracy of memory based recommendation while keeping the low time cost, an expected item bias (EIA) based similarity computation is proposed.  ...  The features of two classical datasets MovieLens and Netflix for recommendation system benchmarking are anglicized.  ...  Memory based method judges the degree a user likes an item based on the ratings the user gives to other items.  ... 
doi:10.14257/ijdta.2014.7.2.13 fatcat:l5sh4ku3n5ba5ey4w5uxlb3pia

Item-based Collaborative Memory Networks for Recommendation

Dewen Seng, Guangsen Chen, Qiyan Zhang
2020 IEEE Access  
However, there is little work on employing memory networks for recommender systems compared with the huge number of literature applying other deep learning architectures for recommenders.  ...  between the user and new items based on the item-item similarity.  ... 
doi:10.1109/access.2020.3039380 fatcat:wecngoslmvcqzgd7uer7bbu6ti

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Ranking Prediction [article]

Aristeidis Karras, Christos Karras
2022 arXiv   pre-print
Similar to factorization machine approaches, the shared layer allows latent factors acquired for people and things to interact with each other.  ...  This work presents a deep model for concurrently learning item attributes and user behaviour from review text.  ...  Prior to deep learning, standard approaches for recommendation systems (RecSys) used collaborative filtering which relies on decomposing users, items (i.e. movies), and ratings into latent feature matrices  ... 
arXiv:2205.06296v3 fatcat:7yy5b4oxovdlvabej2w3kxpd5q

Intent-aware Item-based Collaborative Filtering for Personalised Diversification

Jacek Wasilewski, Neil Hurley
2018 Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization - UMAP '18  
In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective.  ...  We argue that user preferences towards different aspects should be reflected in recommendations produced by the system.  ...  While the item-based kNN method is understood to be more stable, efficient and justifiable, especially in cases where there are many more users than items, recommender systems using this approach will  ... 
doi:10.1145/3209219.3209234 dblp:conf/um/WasilewskiH18 fatcat:n2e3l7xpnzcpvhjvlbpqjz2bbq

Movie genome: alleviating new item cold start in movie recommendation

Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi
2019 User modeling and user-adapted interaction  
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast).  ...  User-generated content, such as tags, can also be rare or absent in CS situations.  ...  In this section, we therefore review the existing, state-of-the-art approaches in content-based multimedia recommender systems (Sect. 2.1) and feature weighting for CS recommender systems (Sect. 2.2) and  ... 
doi:10.1007/s11257-019-09221-y fatcat:cnzhzxwjlfbd7g4hhafalhtji4

Weighted Similarity and Core-User-Core-Item Based Recommendations

Zhuangzhuang Zhang, Yunquan Dong
2022 Entropy  
In traditional recommendation algorithms, the users and/or the items with the same rating scores are equally treated.  ...  We also propose a Core-User-Item Solver (CUIS) to calculate the core users and core items of the system, as well as the weighting coefficients for each user and each item.  ...  Acknowledgments: The authors would like to thank the editors and the reviewers for their insightful comments and suggestions, which resulted in substantial improvements to this work.  ... 
doi:10.3390/e24050609 pmid:35626494 fatcat:ej4n4mh5rzf7pafa73ms5kyqvu

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  
These joint recommendations are then refined by the wisdom from the crowd and projected to the item (or tag) space for final item (or tag) recommendations.  ...  Finally, supported by this user profile, we propose a framework for collaborative filtering in social tagging systems.  ...  Therefore, in an online setting, the time complexity of our approach to make tag recommendations for one <user, item> pair is the same as that of the classical user-based and item-based tag recommendation  ... 
doi:10.1145/1871437.1871541 dblp:conf/cikm/PengZZW10 fatcat:aw2txylqxvb6hln2fofqdj2rdy

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users [article]

Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua
2021 arXiv   pre-print
In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.  ...  Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off.  ...  With V and P established in the beginning of turn , the system takes an action to interact with the user -either asking for attribute(s) or recommending items.  ... 
arXiv:2005.12979v4 fatcat:m4l54jeco5cdtd4vnxtfhicoxy
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