Zobrazeno 1 - 10
of 183
pro vyhledávání: '"Fan, Ziwei"'
Autor:
Zou, Henry Peng, Yu, Gavin Heqing, Fan, Ziwei, Bu, Dan, Liu, Han, Dai, Peng, Jia, Dongmei, Caragea, Cornelia
In e-commerce, accurately extracting product attribute values from multimodal data is crucial for improving user experience and operational efficiency of retailers. However, previous approaches to multimodal attribute value extraction often struggle
Externí odkaz:
http://arxiv.org/abs/2404.08886
Autor:
Rahdari, Behnam, Ding, Hao, Fan, Ziwei, Ma, Yifei, Chen, Zhuotong, Deoras, Anoop, Kveton, Branislav
The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations. However, despite the size of the LLM, most existing model
Externí odkaz:
http://arxiv.org/abs/2312.14345
Sequential recommendation (SR) models the dynamic user preferences and generates the next-item prediction as the affinity between the sequence and items, in a joint latent space with low dimensions (i.e., the sequence and item embedding space). Both
Externí odkaz:
http://arxiv.org/abs/2306.11986
Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of
Externí odkaz:
http://arxiv.org/abs/2306.03191
Autor:
Fan, Ziwei, Liu, Zhiwei, Heinecke, Shelby, Zhang, Jianguo, Wang, Huan, Xiong, Caiming, Yu, Philip S.
Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs
Externí odkaz:
http://arxiv.org/abs/2305.07633
Publikováno v:
SIGIR 2023
Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactio
Externí odkaz:
http://arxiv.org/abs/2304.03344
Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Lei
Externí odkaz:
http://arxiv.org/abs/2301.12197
Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However, historical interaction data is highly sparse, and most items are long-
Externí odkaz:
http://arxiv.org/abs/2301.01737
Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item transitions observed in sequences, which can be viewed as an item rel
Externí odkaz:
http://arxiv.org/abs/2210.13572
Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can attract user
Externí odkaz:
http://arxiv.org/abs/2202.03392