Papers with item recommendation
Unbiasing Review Ratings with Tendency Based Collaborative Filtering (2020.aacl-srw)
Copied to clipboard
| Challenge: | Existing models for rating prediction neglect rating bias due to personal user bias preferences. |
| Approach: | They propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings. |
| Outcome: | The proposed model minimizes the user and item tendency for score prediction along with text review analysis with respect to ratings. |
Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing methods to recommend items are categorized into attribute-based and generation-based methods. |
| Approach: | They propose to represent items in natural language and formulate a conversational recommender system that can be optimized in a single stage without relying on non-textual metadata. |
| Outcome: | The proposed model can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. |
Predicting Stances from Social Media Posts using Factorization Machines (C18-1)
Copied to clipboard
| Challenge: | Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. |
| Approach: | They propose to use factorization machines to model user preferences toward topics from social media data to predict whether a given text/user is in favor (agree), against (disagreer), or neutral toward a target topic. |
| Outcome: | The proposed method can predict stances of silent users based on their stance toward other topics and the social media posts of the user. |
Towards Topic-Guided Conversational Recommender System (2020.coling-main)
Copied to clipboard
| Challenge: | Existing CRS datasets focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. |
| Approach: | They propose a topic-guided conversational recommendation dataset . it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario . |
| Outcome: | The proposed approach is more reasonable and controllable than previous approaches. |
Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
Copied to clipboard
Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
| Challenge: | Recent advances in neural models have shown promising progress on this task, but key challenges remain . |
| Approach: | They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial. |