Leveraging knowledge graph embedding for effective conversational recommendation
Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to the traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then fur...
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| Format: | Thesis-Master by Research |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/155658 |
| _version_ | 1826124018963447808 |
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| author | Xia, Yunwen |
| author2 | Zhang Jie |
| author_facet | Zhang Jie Xia, Yunwen |
| author_sort | Xia, Yunwen |
| collection | NTU |
| description | Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to the traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then further boosts the recommendation performance. However, existing studies on CRS ignore to address the relationship among attributes, users, and items effectively, which might lead to inappropriate questions and inaccurate recommendations. In this view, we propose a knowledge graph (KG)-based conversational recommender system (referred to as KG-CRS). Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, i.e., dynamically changing during the dialogue process by removing negative items or attributes. We then learn the informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph. Extensive experiments on three real datasets validate the superiority of our method in terms of both the recommendation and conversation tasks. |
| first_indexed | 2024-10-01T06:13:48Z |
| format | Thesis-Master by Research |
| id | ntu-10356/155658 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:13:48Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1556582022-04-04T03:16:53Z Leveraging knowledge graph embedding for effective conversational recommendation Xia, Yunwen Zhang Jie School of Computer Science and Engineering ZhangJ@ntu.edu.sg Engineering::Computer science and engineering Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to the traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then further boosts the recommendation performance. However, existing studies on CRS ignore to address the relationship among attributes, users, and items effectively, which might lead to inappropriate questions and inaccurate recommendations. In this view, we propose a knowledge graph (KG)-based conversational recommender system (referred to as KG-CRS). Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, i.e., dynamically changing during the dialogue process by removing negative items or attributes. We then learn the informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph. Extensive experiments on three real datasets validate the superiority of our method in terms of both the recommendation and conversation tasks. Master of Engineering 2022-03-10T04:34:36Z 2022-03-10T04:34:36Z 2022 Thesis-Master by Research Xia, Y. (2022). Leveraging knowledge graph embedding for effective conversational recommendation. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155658 https://hdl.handle.net/10356/155658 10.32657/10356/155658 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Xia, Yunwen Leveraging knowledge graph embedding for effective conversational recommendation |
| title | Leveraging knowledge graph embedding for effective conversational recommendation |
| title_full | Leveraging knowledge graph embedding for effective conversational recommendation |
| title_fullStr | Leveraging knowledge graph embedding for effective conversational recommendation |
| title_full_unstemmed | Leveraging knowledge graph embedding for effective conversational recommendation |
| title_short | Leveraging knowledge graph embedding for effective conversational recommendation |
| title_sort | leveraging knowledge graph embedding for effective conversational recommendation |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/155658 |
| work_keys_str_mv | AT xiayunwen leveragingknowledgegraphembeddingforeffectiveconversationalrecommendation |