Collaborative Filtering: Matrix Completion and Session-Based Recommendation Tasks

Jannach, Dietmar; Zanker, Markus

doi:10.1142/9789813275355_0001

Cited by 4 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many real-world applications, however, such longer-term information is often not available, because users are not logged in or because they are first-time users. In such cases, techniques that leverage behavioral patterns in a community can still be applied (Jannach and Zanker 2019). The difference is that instead of the long-term preference profiles only the observed interactions with the user in the ongoing session can be used to adapt the recommendations to the assumed needs, preferences, or intents of the user.…”

Section: Introductionmentioning

confidence: 99%

Empirical analysis of session-based recommendation algorithms

Ludewig

Mauro

Latifi

et al. 2020

User Model User-Adap Inter

Self Cite

101

View full text Add to dashboard Cite

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning-based (“neural”) approaches to session-based recommendations have been proposed. However, previous research indicates that today’s complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state of the art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.

show abstract

Section: Introductionmentioning

confidence: 99%

Empirical analysis of session-based recommendation algorithms

Ludewig

Mauro

Latifi

et al. 2020

User Model User-Adap Inter

Self Cite

101

View full text Add to dashboard Cite

show abstract

“…Based on this similarity, imputation of missing test labels is made by inferences on incompletely specified values [2].…”

Section: Collaborative Filtering Recommender Systemsmentioning

confidence: 99%

Integration and Recommendation System of Profiles based on Professional Social Networks

Dayang,

Mbouche Bomda

2024

EAI Endorsed Trans Context Aware Syst App

View full text Add to dashboard Cite

The aim of our investigation is to personalize bilateral recommendation of job-related proposals based on existing professional social networks. In a context where the points of view of job seekers and employers can be contradictory, our approach consists in trying to bring the both in a best possible matching. To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions.

show abstract

“…Relevant works are surveyed in [19,20]. Jannach and Zanker (2018) comprehensively reviewed collaborative filtering models in session-based recommendation scenarios [21]. These approaches adopt pairwise ranking loss function and produce Top-N ranked items as output.…”

Section: Related Workmentioning

confidence: 99%

Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information

Xie²,

et al. 2021

Pattern Recognition

View full text Add to dashboard Cite

Collaborative Filtering: Matrix Completion and Session-Based Recommendation Tasks

Cited by 4 publications

References 45 publications

Empirical analysis of session-based recommendation algorithms

Empirical analysis of session-based recommendation algorithms

Integration and Recommendation System of Profiles based on Professional Social Networks

Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information

Contact Info

Product

Resources

About