Dirección de investigación : Matthieu LATAPY

Co-supervisión : TCHUENTE Maurice

Recommender system with temporal dynamics based on link streams

Recommending appropriate items to users is crucial in many e-commerce platforms that propose a large number of items to users. Recommender systems are one favorite solution for this task. Most research in this area is based on explicit ratings that users give to items, while most of the time, ratings are not available in sufficient quantities. In these situations, it is important that recommender systems use implicit data which are link stream connecting users to items while maintaining timestamps i.e. users browsing, purchases and streaming history. We exploit this type of implicit data in this thesis.
One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like content-based features of items, past interest of users for items and trust between users. However, they often use only one or two such pieces of information simultaneously, which can limit their performance because user's interest for an item can depend on more than two types of side information.
To address this limitation, we make three contributions in the field of graph-based recommender systems. The first one is an extension of the Session-based Temporal Graph (STG) introduced by Xiang et al., which is a dynamic graph combining long-term and short-term preferences in order to better capture user preferences over time. STG ignores content-based features of items, and make no difference between the weight of newer edges and older edges. The new proposed graph Time-weight Content-based STG addresses STG limitations by adding a new node type for content-based features of items, and a penalization of older edges.
The second contribution is the Link Stream Graph (LSG) for temporal recommendations. This graph is inspired by a formal representation of link stream, and has the particularity to consider time in a continuous way unlike others state-of-the-art graphs, which ignore the temporal dimension like the classical bipartite graph (BIP), or consider time discontinuously like STG where time is divided into slices.
The third contribution in this thesis is GraFC2T2, a general graph-based framework for top-N recommendation. This framework integrates basic recommender graphs, and enriches them with content-based features of items, users' preferences temporal dynamics, and trust relationships between them. Implementations of these three contributions on CiteUlike, Delicious, Last.fm, Ponpare, Epinions and Ciao datasets confirm their relevance.

Publicaciones 2015-2019

• 2019
  • A. Nzekon Nzeko'o : “Système de recommandation avec dynamique temporelle basée sur les flots de liens”, tesis, defensa 09/12/2019, dirección de investigación Latapy, Matthieu, co-supervisión : Tchuente, Maurice (2019)
  • A. Nzekon Nzeko'O, M. Tchuente, M. Latapy : “Link stream graph for temporal recommendations”, (2019)
  • A. Nzekon Nzeko'O, M. Tchuente, M. Latapy : “A general graph-based framework for top-N recommendation using content, temporal and trust information”, Journal of Interdisciplinary Methodologies and Issues in Science, vol. Analysis of networks and graphs, Graph and network analysis, (Éditions universitaires d’Avignon) (2019)
• 2017
  • A. Nzekon Nzeko'O, M. Tchuente, M. Latapy : “Time Weight Content-Based Extensions of Temporal Graphs for Personalized Recommendation”, WEBIST 2017, Porto, Portugal (2017)
• 2015
  • A. Nzekon Nzeko'O, M. Latapy, M. Tchuente : “Social Network Analysis of Developers’ and Users’ Mailing Lists of Some Free Open Source Software”, 2015 IEEE International Congress on Big Data (BigData Congress), New York City, United States, pp. 728-732, (IEEE) (2015)