4 research outputs found
Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations
The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, current recommendation methods based on graph
embeddings have shown state-of-the-art performance. These methods commonly
encode latent rating patterns and content features. Different from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Our approach has the advantage of
providing explanations which leverage aspect-based opinions given by users
about recommended items. Furthermore, we also provide examples of the
applicability of recommendations utilizing aspect opinions as explanations in a
visualization dashboard, which allows obtaining information about the most and
least liked aspects of similar users obtained from the embeddings of an input
graph
Recommendations by Concise User Profiles from Review Text
Recommender systems are most successful for popular items and users with
ample interactions (likes, ratings etc.). This work addresses the difficult and
underexplored case of supporting users who have very sparse interactions but
post informative review texts. Our experimental studies address two book
communities with these characteristics. We design a framework with
Transformer-based representation learning, covering user-item interactions,
item content, and user-provided reviews. To overcome interaction sparseness, we
devise techniques for selecting the most informative cues to construct concise
user profiles. Comprehensive experiments, with datasets from Amazon and
Goodreads, show that judicious selection of text snippets achieves the best
performance, even in comparison to LLM-generated rankings and to using LLMs to
generate user profiles
Using consumer feedback from location-based services in PoI recommender systems for people with autism
When suggesting Points of Interest (PoIs) to people with autism spectrum
disorders, we must take into account that they have idiosyncratic sensory
aversions to noise, brightness and other features that influence the way they
perceive places. Therefore, recommender systems must deal with these aspects.
However, the retrieval of sensory data about PoIs is a real challenge because
most geographical information servers fail to provide this data. Moreover,
ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical
areas and lack sustainability. Thus, we investigate the extraction of sensory
data about places from the consumer feedback collected by location-based
services, on which people spontaneously post reviews from all over the world.
Specifically, we propose a model for the extraction of sensory data from the
reviews about PoIs, and its integration in recommender systems to predict item
ratings by considering both user preferences and compatibility information. We
tested our approach with autistic and neurotypical people by integrating it
into diverse recommendation algorithms. For the test, we used a dataset built
in a crowdsourcing campaign and another one extracted from TripAdvisor reviews.
The results show that the algorithms obtain the highest accuracy and ranking
capability when using TripAdvisor data. Moreover, by jointly using these two
datasets, the algorithms further improve their performance. These results
encourage the use of consumer feedback as a reliable source of information
about places in the development of inclusive recommender systems