Evaluation of Time-Aware Recommender Systems Techniques for Neighborhood- Based Models in Session-Based Recommendations
Traditional works in Recommender Systems focus on making personalized recommendations of new items of interest to users of a database. However, with the growth of streamming platforms and services offered online, generating recommendations for the current user session, considering his/her context, b...
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| Format: | master thesis |
| Status: | Published version |
| Publication Date: | 2021 |
| Country: | Brasil |
| Institution: | Universidade de São Paulo (USP) |
| Repository: | Biblioteca Digital de Teses e Dissertações da USP |
| Language: | English |
| OAI Identifier: | oai:teses.usp.br:tde-26042021-135226 |
| Online Access: | https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26042021-135226/ |
| Access Level: | Open access |
| Keyword: | Contextos temporais Modelos de vizinhança Neighborhood-based models Recommender systems Session-based recommender systems Sistemas de recomendação Sistemas de recomendação basea- da em sessão Temporal contexts |
| Summary: | Traditional works in Recommender Systems focus on making personalized recommendations of new items of interest to users of a database. However, with the growth of streamming platforms and services offered online, generating recommendations for the current user session, considering his/her context, became a big area of interest in the area. Session-Based Recommender Systems focus on recommending items to a user session. When the user has a history of interactions with the platform, previous sessions can be used to infer the users preference. Nevertheless, these platforms can also attract and regularize users by recommending items of interest to anonymous users, usually passerbies, who have only the information available of the current session to make recommendations. This work investigated the use of temporal contexts in Session-Based Recommender Systems, focusing in anonymous sessions recommendations, using neighborhoodbased models, which are among the state-of-the-art models in this task. For this to happen, we performed a deep analysis of the existing neighborhood-based models and analyzed how the application of various contexts in these algorithms influences their performance, as well as providing insights about the interactions made in a given dataset. |
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