Contrastive Learning for Cold-Start Recommendation

Wei, Yinwei; Xiang, Wang; Li, Qi; Nie, Liqiang; Li, Yan; Li, Xuanping; Chua, Tat-Seng

doi:10.1145/3474085.3475665

Cited by 208 publications

(77 citation statements)

References 34 publications

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“…), and corrects and optimizes its recommendation results based on the feedback results of interaction with users (clicks, ratings, etc.) [17,18].…”

Section: Related Workmentioning

confidence: 99%

Implicit semantic-based personalized micro-videos recommendation

Liu¹

2022

Preprint

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With the rapid development of mobile Internet and big data, a huge amount of data is generated in the network, but the data that users are really interested in a very small portion. To extract the information that users are interested in from the huge amount of data, the information overload problem needs to be solved.In the era of mobile internet, the user's characteristics and other information should be combined in the massive amount of data to quickly and accurately recommend content to the user, as far as possible to meet the user's personalized needs. Therefore, there is an urgent need to realize high-speed and effective retrieval in tens of thousands of micro-videos. Video data content contains complex meanings, and there are intrinsic connections between video data. For multimodal information, subspace coding learning is introduced to build a coding network from public potential representations to multimodal feature information, taking into account the consistency and complementarity information under each modality to obtain a public representation of the complete eigenvalue. An end-to-end reordering model based on deep learning and attention mechanism, called interest-related product similarity model based on multimodal data, is proposed for providing top-N recommendations.The multimodal feature learning module, interest-related network module and product similarity recommendation module together form the new model.By conducting extensive experiments on publicly accessible datasets, the results demonstrate the state-of-the-art performance of our proposed algorithm and its effectiveness.

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“…), and corrects and optimizes its recommendation results based on the feedback results of interaction with users (clicks, ratings, etc.) [17,18].…”

Section: Related Workmentioning

confidence: 99%

Implicit semantic-based personalized micro-videos recommendation

Liu¹

2022

Preprint

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“…In the multimodal recommendation , the main currently used recommendation models are divided into collaborative filtering-based video recommendation [24], contentbased video recommendation [25] and hybrid video recommendation [26]. YouTube [27] in 2008 used User-Video-based graph tour algorithm is a kind of propagation diffusion of video labels on the graph of collaborative filtering algorithm, but only the video label information is considered, and there is a cold start problem [28]. Mei et al [29] designed a contextual video recommendation system based on multimodal content relevance and user feedback, considering the different composition of the video and the different levels of user interest in different parts of the video, and seamlessly integrating multimodal relevance and user feedback through relevance feedback and attention fusion, but ignoring the role of user.…”

Section: Multimodal Recommendationmentioning

confidence: 99%

A multimedia recommendation model based on collaborative graph

Lim¹,

Bansal²,

Buru³

et al. 2022

Preprint

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As one of the main solutions to the information overload problem, recommender systems are widely used in daily life. In the recent emerging micro-video recommendation scenario, micro-videos contain rich multimedia information, involving text, image, video and other multimodal data, and these rich multimodal information conceals users' deep interest in the items. Most of the current recommendation algorithms based on multimodal data use multimodal information to expand the information on the item side, but ignore the different preferences of users for different modal information, and lack the fine-grained mining of the internal connection of multimodal information. To investigate the problems in the micro-video recommendr system mentioned above, we design a hybrid recommendation model based on multimodal information, introduces multimodal information and user-side auxiliary information in the network structure, fully explores the deep interest of users, measures the importance of each dimension of user and item feature representation in the scoring prediction task, makes the application of graph neural network in the recommendation system is improved by using an attention mechanism to fuse the multi-layer state output information, allowing the shallow structural features provided by the intermediate layer to better participate in the prediction task. The recommendation accuracy is improved compared with the traditional recommendation algorithm on different data sets, and the feasibility and effectiveness of our model is verified.

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“…We set the number of interests to [1,2,4,8,16] successively and conduct experiments. The experimental results are shown in Table 4.…”

Section: Effect Of the Number Of Interestsmentioning

confidence: 99%

“…It has obtained the success in computer vision [2], natural language processing [4,5,19] and other domains [13]. More recently, contrastive learning also has been introduced to the recommendation, such as sequential recommendation, recommendation based on graph neural network, and etc., which realizes the debiasing [22] and the denoising [14], and resolves the representation degeneration [15] and the cold start problem [16], improving the recommendation accuracy [10,18,20,21]. We note that there exists noise in the positive interactions in the micro-video scenario since micro-videos are automatically played and sometimes users cannot judge whether they like the micro-video or not until the micro-video finishes playing.…”

Section: Introductionmentioning

confidence: 99%

Improving Micro-video Recommendation via Contrastive Multiple Interests

Li¹,

Jin²,

Song³

et al. 2022

Preprint

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With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multiple interests in microvideos. Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. Specifically, CMI learns multiple interest embeddings for each user from his/her historical interaction sequence, in which the implicit orthogonal micro-video categories are used to decouple multiple user interests. Moreover, it establishes the contrastive multi-interest loss to improve the robustness of interest embeddings and the performance of recommendations. The results of experiments on two micro-video datasets demonstrate that CMI achieves state-of-theart performance over existing baselines. CCS CONCEPTS• Information systems → Recommender systems.

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Contrastive Learning for Cold-Start Recommendation

Cited by 208 publications

References 34 publications

Implicit semantic-based personalized micro-videos recommendation

Implicit semantic-based personalized micro-videos recommendation

A multimedia recommendation model based on collaborative graph

Improving Micro-video Recommendation via Contrastive Multiple Interests

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