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Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates [article]

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Jonathan Weill, Noam Koenigstein
2021 arXiv   pre-print
In this work, we wish to revisit the cold start problem in order to draw attention to an overlooked challenge: the ability to integrate and balance between (regular) warm items and completely cold items  ...  Over the years, a variety of hybrid recommendation models have been proposed to address this problem by utilizing items' metadata and content along with their ratings or usage patterns.  ...  Cold Balancing (P1) We begin by evaluating the ability of CWH to conduct an adaptable integration of multiple cold items into an existing model (of warm items) and produce recommendation lists that include  ... 
arXiv:2112.07615v1 fatcat:6ltyommr6ngjnbtlx4xgj5zyem

Deep Meta-learning in Recommendation Systems: A Survey [article]

Chunyang Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, Jiadi Yu, Feilong Tang
2022 arXiv   pre-print
user cold-start and item cold-start.  ...  However, since model training from scratch requires sufficient data, deep learning-based recommendation methods still face the bottlenecks of insufficient data and computational inefficiency.  ...  By treating POI recommendation in each city as a task, CHAML extends the MAML framework to learn the initial weights of an attention-based sequential recommendation model in order to quickly adapt to cold-start  ... 
arXiv:2206.04415v1 fatcat:w5rax6bjy5efjfmxunvf4j6kly

Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings [article]

Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He
2019 arXiv   pre-print
In this paper, we aim to improve CTR predictions during both the cold-start phase and the warm-up phase when a new ad is added to the candidate pool.  ...  Experimental results on three real-world datasets showed that Meta-Embedding can significantly improve both the cold-start and warm-up performances for six existing CTR prediction models, ranging from  ...  We seek to improve both cold-start and warm-up performance by leveraging a unified loss function for training.  ... 
arXiv:1904.11547v1 fatcat:3qfjoozpszfehmuzls5tobrj5u

Neural content-aware collaborative filtering for cold-start music recommendation [article]

Paul Magron, Cédric Févotte
2022 arXiv   pre-print
These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history.  ...  Experimental results show that the proposed method reaches state-of-the-art results for a cold-start music recommendation task.  ...  What is the impact of the interaction model (shallow vs. deep) onto performance for both warm-and cold-start recommendation? 5.  ... 
arXiv:2102.12369v3 fatcat:ysyk7nlqtbabdaa6hs25jfu3zy

Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks [article]

Tieyun Qian, Yile Liang, Qing Li
2020 arXiv   pre-print
Empirical results on two real-world datasets demonstrate that our model yields significant improvements for cold start recommendations and outperforms or matches state-of-the-arts performance in the warm  ...  This leads to the capability of learning embeddings for cold start users/items.  ...  Settings We examine the model performance in both the cold and warm start scenario.  ... 
arXiv:1912.12398v2 fatcat:kmsepe7jtrb7jhjsywz7lvgseu

Cross-domain User Preference Learning for Cold-start Recommendation [article]

Huiling Zhou, Jie Liu, Zhikang Li, Jin Yu, Hongxia Yang
2021 arXiv   pre-print
Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems.  ...  Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation.  ...  Among all, cross-domain recommendation [10, 20, 46] and cold-start recommendation [7, 30, 34] problems draw a lot of attention.  ... 
arXiv:2112.03667v1 fatcat:fvg2amg5qber7encbsxgosxgtu

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.  ...  However, existing bandit-based methods model recommendation actions homogeneously.  ...  However, to the best of our knowledge, few research has conduct conclusive discussions on the comparison between the bandit-based and warm-start recommendation systems in terms of the degree of cold start  ... 
arXiv:2005.12979v4 fatcat:m4l54jeco5cdtd4vnxtfhicoxy

Enhancing Video Recommendation Using Multimedia Content [chapter]

Yashar Deldjoo
2019 SpringerBriefs in Applied Sciences and Technology  
To date, majority of movie recommender systems use collaborative filtering (CF) models or content-based filtering (CBF) relying on metadata (e.g., editorial such as genre or wisdom of the crowd such as  ...  Variety of tasks related to movie recommendation using multimedia content have been studied.  ...  Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a usercentric  ... 
doi:10.1007/978-3-030-32094-2_6 fatcat:ejusjykphfbe3jwes6u3r55uxe

Unified YouTube Video Recommendation via Cross-network Collaboration

Ming Yan, Jitao Sang, Changsheng Xu
2015 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15  
Similar to general recommender systems, Y-ouTube video recommendation suffers from typical problems like new user, cold-start, data sparsity, etc.  ...  network-based recommendation solutions.  ...  A unified video recommendation framework is presented, with goals to simultaneously address three longstanding problems in recommender system, i.e., new user, cold-start and sparsity. 3.  ... 
doi:10.1145/2671188.2749344 dblp:conf/mir/YanSX15 fatcat:p4rtjokbsnd2fdvwh4zrq7jqli

Learning to Style-aware Bayesian Personalized Ranking for Visual Recommendation

Ming He, Shaozong Zhang, Qian Meng
2019 IEEE Access  
To bridge this gap, we propose introducing style feature modeling, which is highly relevant with user preference, into the visual recommendation model.  ...  Recently, product images have been gaining the attention of recommender system researchers in the field of visual recommendation.  ...  There are two types of evaluation settings during the test process: warm-start and cold-start.  ... 
doi:10.1109/access.2019.2892984 fatcat:4f3dawk7tvbexpqen4tiuctgaa

Recurrent knowledge graph embedding for effective recommendation

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, Chi Xu
2018 Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18  
Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge.  ...  Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into  ...  This could be explained by the fact that different from other cold-start cases where cold-start users possess similar amount of external information as warm-start users, in recommendation with KGs, cold-start  ... 
doi:10.1145/3240323.3240361 dblp:conf/recsys/Sun00BHX18 fatcat:z5pg6lacwjd4lmqx7i2ry5psme

Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision [article]

Ali Sadeghian, Shervin Minaee, Ioannis Partalas, Xinxin Li, Daisy Zhe Wang, Brooke Cowan
2019 arXiv   pre-print
We show empirically that our model generates high-quality representations that boost the performance of a hotel recommendation system in addition to other applications.  ...  An important advantage of the proposed neural model is that it addresses the cold-start problem for hotels with insufficient historical click information by incorporating additional hotel attributes which  ...  We would also like to thank Dan Friedman and Thomas Mulc for providing useful comments and feedback.  ... 
arXiv:1910.03943v1 fatcat:jhr6nlg6zjdzlhlu4lcpk4zoi4

Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems [article]

Shuo Lin, Jianling Wang, Ziwei Zhu, James Caverlee
2022 arXiv   pre-print
Reconstruction via Attribute Mapping, to improve the modeling of cold-start items; and (iii) Dual-Policy Learning, to better guide the CRS when dealing with either popular or unpopular items.  ...  to a more personalized recommendation.  ...  Formally, Let 𝑉 + denote the set of warm-start items. Let 𝑒𝑚𝑏 𝑖𝑡𝑒𝑚 𝑖 + and 𝑒𝑚𝑏 𝑖𝑡𝑒𝑚 𝑖 − denote the item embedding for a warm-start and a cold-start item respectively.  ... 
arXiv:2208.03298v1 fatcat:5qxzsqlz4fh3ppkjfj7iqxckrm

Socially-aware Dual Contrastive Learning for Cold-Start Recommendation

Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
In this work, we propose sociallyaware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users.  ...  Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias.  ...  ACKNOWLEDGMENTS We would like to thank the reviewers for their comments. Jing Du is supported by Chinese Scholarship Council(CSC) under grant (No. 202006290008).  ... 
doi:10.1145/3477495.3531780 fatcat:flk7wglh25fhza4imt5x3repn4

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  
Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric  ...  and leverages the learned model on the movie genome to recommend cold items (items without interactions).  ...  Experimental study A: Offline experiment In this experiment, we investigate offline recommendation in cold-and warm-start scenarios.  ... 
doi:10.1007/s11257-019-09221-y fatcat:cnzhzxwjlfbd7g4hhafalhtji4
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