Attentive Group Recommendation

Cao, Da; He, Xiangnan; Miao, Lianhai; An, Yarui; Yang, Chao; Hong, Richang

doi:10.1145/3209978.3209998

Cited by 204 publications

(183 citation statements)

References 34 publications

Supporting

Mentioning

183

Contrasting

Order By: Relevance

“…Hence, we design GUPM to model the overall user preferences in the list by treating items equally important, no matter at which time step the items have been curated. A natural way is to incorporate another learnable vector h ∈ R d as the query, normally referred to as global or context vector in the literature [2,5]. We have:…”

Section: Attention-based User Preference Modelmentioning

confidence: 99%

Consistency-Aware Recommendation for User-Generated Item List Continuation

Zhang

Liu

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or "boards") on Pinterest, book-based lists on Goodreads, and answerbased lists on question-answer forums like Zhihu. As users create these lists, a common challenge is in identifying what items to curate next. Some lists are organized around particular genres or topics, while others are seemingly incoherent, reflecting individual preferences for what items belong together. Furthermore, this heterogeneity in item consistency may vary from platform to platform, and from sub-community to sub-community. Hence, this paper proposes a generalizable approach for user-generated item list continuation. Complementary to methods that exploit specific content patterns (e.g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e.g., videos, images, books). A key contribution is in intelligently combining two preference models via a novel consistency-aware gating network -a general user preference model that captures a user's overall interests, and a current preference priority model that captures a user's current (as of the most recent item) interests. In this way, the proposed consistency-aware recommender can dynamically adapt as user preferences evolve. Evaluation over four datasets (of songs, books, and answers) confirms these observations and demonstrates the effectiveness of the proposed model versus stateof-the-art alternatives. Further, all code and data are available at https://github.com/heyunh2015/ListContinuation_WSDM2020.

show abstract

Section: Attention-based User Preference Modelmentioning

confidence: 99%

Consistency-Aware Recommendation for User-Generated Item List Continuation

Zhang

Liu

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

show abstract

“…Chen et al [5] propose an attentive collaborative filtering framework, where each item is segmented into component-level elements, and attention scores are learned for these components for obtaining a better representation of items. Attention networks are also applied in group recommendation [2], sequential recommendation [37], review-based recommendation [4,29,32] and context-aware recommendation [26].…”

Section: Related Workmentioning

confidence: 99%

“…where p u ∈ R d is the final representation of user u. The intention is to consider the general user preference (modeled by e u ) that in some cases cannot be fully characterized by the aggregation of the list representations, which has been proven beneficial for recommendation in [2,4,5]. Similarly, we have:…”

Section: Rq3: Recommending Top-k Item Listsmentioning

confidence: 99%

“…In this layer, the estimated preference scorer ul is calculated given the user latent representation p u and the list latent representation q l . Following [2,10], we also first apply the element-wise product on p u and q l , and then concatenate it with the original representations:…”

Section: Rq3: Recommending Top-k Item Listsmentioning

confidence: 99%

“…where the element-wise product p u ⊙ q l captures the interactive relationship between the user and the list which follows traditional latent factor models. The concatenation of the original representations p u and q l is to prevent information loss due to the elementwise product which has been proven effective for recommendation in [2,10]. After that, we apply a two-layer neural network to obtain the final preference score:…”

Section: Rq3: Recommending Top-k Item Listsmentioning

confidence: 99%

See 2 more Smart Citations

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Wang

Niu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images on Pinterest, and lists of answers on question-answer sites like Zhihu. Recommending item lists is critical for increasing user engagement and connecting users to new items, but many approaches are designed for the item-based recommendation, without careful consideration of the complex relationships between items and lists. Hence, in this paper, we propose a novel user-generated list recommendation model called AttList. Two unique features of AttList are careful modeling of (i) hierarchical user preference, which aggregates items to characterize the list that they belong to, and then aggregates these lists to estimate the user preference, naturally fitting into the hierarchical structure of item lists; and (ii) item and list consistency, through a novel self-attentive aggregation layer designed for capturing the consistency of neighboring items and lists to better model user preference. Through experiments over three real-world datasets reflecting different kinds of user-generated item lists, we find that AttList results in significant improvements in NDCG, Precision@k, and Recall@k versus a suite of state-ofthe-art baselines. Furthermore, all code and data are available at https://github.com/heyunh2015/AttList.

show abstract

Self‐supervised graph learning for occasional group recommendation

Hao¹,

Yin²,

Zhang³

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold‐start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high‐quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high‐order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high‐order neighbors with few interactions. Motivated by the self‐supervised learning technique, which is able to find the correlations within the data itself, we propose a self‐supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold‐start users/items/groups. To explicitly enhance the high‐order cold‐start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self‐attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state‐of‐the‐art methods.

show abstract

Attentive Group Recommendation

Cited by 204 publications

References 34 publications

Consistency-Aware Recommendation for User-Generated Item List Continuation

Consistency-Aware Recommendation for User-Generated Item List Continuation

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Self‐supervised graph learning for occasional group recommendation

Contact Info

Product

Resources

About