2021
DOI: 10.1007/s00779-021-01606-4
|View full text |Cite
|
Sign up to set email alerts
|

Diversification in session-based news recommender systems

Abstract: Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
19
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3
3
1

Relationship

3
4

Authors

Journals

citations
Cited by 15 publications
(19 citation statements)
references
References 36 publications
0
19
0
Order By: Relevance
“…The effectiveness of these assumptions and learning approaches differs across different applications. For instance, the pair-wise 9 Users with few interactions are omitted from experiments.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…The effectiveness of these assumptions and learning approaches differs across different applications. For instance, the pair-wise 9 Users with few interactions are omitted from experiments.…”
Section: Resultsmentioning
confidence: 99%
“…-Beyond accuracy evaluation: In this paper we only used user-item interactions. Future approaches could include additional information and relevant stakeholders so that fairness [12] and diversity [9] are also taken into account.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…-Diversity: A diverse list of items is often associated to better performance. In some domains such as news [8,7] and music [9] recommendations, diversity is an important factor that should be considered in the model. -Implicit feedback: We have relied on explicit feedback from users.…”
Section: Free Ratingsmentioning
confidence: 99%
“…Coverage measures how well the item catalog [63] or stakeholders [64] are covered in recommendation lists. Diversity measures can be applied, for instance, in news recommendations [65,66] or music recommendations [67] to measure to what extent the recommendation lists contain diverse content. Serendipity metrics measure the novelty and unexpectedness of recommendation lists generated by RSs [68].…”
Section: Evaluation Measures and Baselinesmentioning
confidence: 99%