Recommender Systems (RS) are tools that are often utilized and need constant development in both the structure and the data to use. Impressions Data are a new type of information that is underused and can be helpful in various scenarios. Therefore, we propose a hybrid RS that uses Impressions to mitigate the significant issues in our original system, Knowledge Graph Attention Network (KGAT). The first problem is the situation of complete cold-start, for which we propose the use of questions on selected meaningful attributes and a BERT-based Content-Based RS to perform recommendations following the user’s choices. After that, when in a framework of semi cold-start, the recommendations will be enhanced by using Impressions to rerank the following ones and, from these interactions, to build a profile to use with KGAT. The last issue we will address is the need for more interpretation of negative interactions through the Knowledge Graph, that is, recommendations presented but not chosen. To solve this issue, we use the Impression Discounting model on the set of recommendations produced by KGAT
Garavaglia, M., Solinas, A., Aragon, R., Bandini, S., Epifania, F. (2023). The Use of Impressions in Recommender Systems: Improving Complete and Semi Cold-Start. In Proceedings of the 3rd Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2023) co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AI*IA 2023). CEUR-WS.
The Use of Impressions in Recommender Systems: Improving Complete and Semi Cold-Start
Garavaglia M.
;Bandini S.;Epifania F.
2023
Abstract
Recommender Systems (RS) are tools that are often utilized and need constant development in both the structure and the data to use. Impressions Data are a new type of information that is underused and can be helpful in various scenarios. Therefore, we propose a hybrid RS that uses Impressions to mitigate the significant issues in our original system, Knowledge Graph Attention Network (KGAT). The first problem is the situation of complete cold-start, for which we propose the use of questions on selected meaningful attributes and a BERT-based Content-Based RS to perform recommendations following the user’s choices. After that, when in a framework of semi cold-start, the recommendations will be enhanced by using Impressions to rerank the following ones and, from these interactions, to build a profile to use with KGAT. The last issue we will address is the need for more interpretation of negative interactions through the Knowledge Graph, that is, recommendations presented but not chosen. To solve this issue, we use the Impression Discounting model on the set of recommendations produced by KGAT
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