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Found 14 papers, 2 papers with code
Justification of Recommender Systems Results: A Service-based Approach
In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results.
Using consumer feedback from location-based services in PoI recommender systems for people with autism
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.
User and Item-aware Estimation of Review Helpfulness
We propose a novel helpfulness estimation model that extends previous ones with the analysis of deviations in rating, length and polarity with respect to the reviews written by the same person, or concerning the same item.
Session-aware Recommendation: A Surprising Quest for the State-of-the-art
Recommender systems are designed to help users in situations of information overload.
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets.
Personalized Recommendation of PoIs to People with Autism
The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.
Map-Based Visualization of 2D/3D Spatial Data via Stylization and Tuning of Information Emphasis
In Geographical Information search, map visualization can challenge the user because results can consist of a large set of heterogeneous items, increasing visual complexity.
Impact of Semantic Granularity on Geographic Information Search Support
We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space.
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests
The experiments show that ECCF outperforms U2UCF and category-based collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage.
Concept-aware Geographic Information Retrieval
We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary.
Session-based Suggestion of Topics for Geographic Exploratory Search
Moreover, complex information spaces, such as those managed by Geographical Information Systems, can disorient people, making it difficult to find relevant data.
Multi-faceted Trust-based Collaborative Filtering
The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation.
Empirical Analysis of Session-Based Recommendation Algorithms
However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy.
A Compositional Model of Multi-faceted Trust for Personalized Item Recommendation
In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain: 1 - We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains.
