Cold Start in Recommender Systems—A Survey from Domain Perspective

Sethi, Rachna; Mehrotra, Monica

doi:10.1007/978-981-15-9509-7_19

Cited by 13 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…all three evaluation measures. For implicit datasets, as reported in Table 4, MVAE has the best performance in all three evaluation measures compared to the other CF-based 3 Mean Average Precision 4 Normalized discounted Cumulative Gain 5 Mean Absolute Error RSs. Therefore, we select SLIM and MVAE to reconstruct the interaction matrix between users and items for datasets with explicit and implicit feedback, respectively.…”

Section: Resultsmentioning

confidence: 98%

“…MAP and NDCG are rank-sensitive relevance measures. In the second step, we use MAE 5 averaged over the targets to evaluate predictions of MTRs. Finally, NDCG and MAP are used to evaluate the proposed approach and the other competitor methods that address the cold-start problem.…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…In this paper we focus on hard cold-start entities as they are the most challenging. Apart from cold-start entities, the system cold-start problem may occur [5], where a new RS is introduced to the users, which is not the focus of this paper.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

2022

View full text Add to dashboard Cite

The cold-start problem is one of the main challenges in recommender systems and specifically in collaborative filtering methods. Such methods, albeit effective, typically can not handle new items or users that do not have any prior interaction activity in the system. In this paper, we propose a novel twostep approach to address the cold-start problem. First, we view the user-item interactions in a positive unlabeled (PU) learning setting and reconstruct the interaction matrix between users and warm items, detecting missing links and recommending warm items to existing users. Second, an inductive multi-target regressor is trained on this reconstructed interaction matrix and subsequently predicts interactions for new items that enter the system. To the best of our knowledge, this is the first time that such a two-step PU learning method is proposed to address the cold-start problem in recommender systems. To evaluate the proposed approach, we employed four benchmark datasets from movie and news recommendation domains with explicit and implicit feedback. We compared our method against three other competitor approaches that address the cold-start problem and showed that our proposed method significantly outperforms them, achieving in a case an increase of 16.9% in terms of NDCG.INDEX TERMS recommender systems, collaborative filtering, cold-start problem, PU learning I. INTRODUCTION

show abstract

Section: Resultsmentioning

confidence: 98%

Section: Evaluation Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

2022

View full text Add to dashboard Cite

show abstract

“…Developing recommender systems requires surmounting the sparsity problem [16], [17] and cold start problem [18]- [21] encountered by recommendation models, the core component of recommender systems. The rationale for recommendation models lies in the accurate inference for user's VOLUME 4, 2016 preferences for items, the prerequisite for well recommendation performance, by analyzing observed user-item relations, among which user-item interactions (Sec.…”

Section: Introductionmentioning

confidence: 99%

Recommender Systems Based on Graph Embedding Techniques: A Review

Deng

2022

IEEE Access

View full text Add to dashboard Cite

As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.

show abstract

“…Developing recommender systems requires surmounting the sparsity problem [16,17] and cold start problem [18][19][20][21] encountered by recommendation models, the core component of recommender systems. The rationale for recommendation models lies in the accurate inference for user's preferences for items, the prerequisite for well recommendation performance, by analyzing observed user-item relations, among which user-item interactions (Sec.…”

Section: Introductionmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

View full text Add to dashboard Cite

Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

show abstract

Cold Start in Recommender Systems—A Survey from Domain Perspective

Cited by 13 publications

References 31 publications

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

Recommender Systems Based on Graph Embedding Techniques: A Review

Recommender systems based on graph embedding techniques: A comprehensive review

Contact Info

Product

Resources

About