Combining semantic and linguistic representations for media recommendation

Harrando, Ismail; Troncy, Raphaël
Multimedia Systems Journal, Special issue on data-driven Personalization of television content, Vol.28, N°6, December 2022

Content-based recommendation systems offer the possibility of promot-
ing media (e.g. posts, videos, podcasts) to users based solely on a repre-
sentation of the content (i.e. without using any user-related data such as
views or interactions between users and items). In this work, we study the
potential of using different textual representations (based on the content
of the media) and semantic representations (created from a knowledge
graph of media metadata). We also show that by using off-the-shelf auto-
matic annotation tools from the Information Extraction literature, we
can improve recommendation performance, without any extra cost of
training, data collection or annotation. We first evaluate multiple textual
content representations on two tasks of recommendation: user-specific,
which is performed by suggesting new items to the user given a history of
interactions, and item-based, which is based solely on content relatedness,
and is rarely investigated in the literature of recommender systems. We
compare how using automatically extracted content (via ASR) compares
to using human-written summaries. We then derive a semantic content
representation by combining manually created metadata and automat-
ically extracted annotations and we show that Knowledge Graphs,
through their embeddings, constitute a great modality to seamlessly
integrate extracted knowledge to legacy metadata and can be used to
provide good content recommendations. We finally study how combining
both semantic and textual representations can lead to superior perfor-
mance on both recommendation tasks. Our code is available at https:
//github.com/D2KLab/ka-recsys to support experiment reproducibility.

DOI
HAL
Type:
Journal
Date:
2022-07-15
Department:
Data Science
Eurecom Ref:
6916
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Multimedia Systems Journal, Special issue on data-driven Personalization of television content, Vol.28, N°6, December 2022 and is available at : http://dx.doi.org/10.1007/s00530-022-00968-w

PERMALINK : https://www.eurecom.fr/publication/6916