Blended Recommending

Loepp, Benedikt; Herrmanny, Katja; Ziegler, Jürgen

doi:10.1145/2702123.2702496

Cited by 42 publications

(10 citation statements)

References 36 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In hybrid recommender systems, the presentation of results aims at explaining suggestions under multiple perspectives of relevance. MyMovieFinder [20] separately shows the recommenders that support a suggested item, while RelevanceTuner [38] uses stackable bars to integrate this type of information into a compact view. TalkExplorer [39] and IntersectionExplorer [4] use bidimensional graphs, or grid layouts, to show multiple dimensions of relevance.…”

Section: Explaining/justifying Recommendationsmentioning

confidence: 99%

A Service-oriented Perspective on the Summarization of Recommendations: Preliminary Experiment

Mauro

Ardissono

et al. 2021

Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

The explanation and justification of recommender systems' results are challenging research tasks. On the one hand, a model-based description that clarifies the reasoning approach behind the suggestions might be difficult to understand, or it might fail to convince the user, if (s)he does not agree on the applied inference mechanism. On the other hand, an aspect-based justification based on few characteristics might provide a partial view of items or, if more detailed, it might overload the user with too much information.In order to address these issues, we propose a visual model aimed at justifying recommendations from a holistic perspective. Our model is based on a service-oriented summary of consumers' experience with items. We use the Service Journey Maps to extract data about the experience with services from online reviews, and to generate a visual summary of such feedback, based on evaluation dimensions that refer to all the stages of service fruition. Thanks to a graphical representation of these dimensions (based on bar graphs), and on the provision of on-demand data about the associated aspects of items, our model enables the user to overview the recommendation list and to quickly identify the subset of results that deserve to be inspected in detail for a final selection decision. A preliminary user study, based on the Apartment Monitoring application, has provided encouraging results about the usefulness and efficacy of our model to enhance user awareness and decisionmaking in the presence of medium-size recommendation lists. CCS CONCEPTS• Information systems → Web searching and information discovery; Recommender systems.

show abstract

Section: Explaining/justifying Recommendationsmentioning

confidence: 99%

A Service-oriented Perspective on the Summarization of Recommendations: Preliminary Experiment

Mauro

Ardissono

et al. 2021

Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

show abstract

“…The recommenders are mapped to facets and the user can specify their weight in the generation of recommendations. Systems differ in the visualization of results; e.g., similar to our model, MyMovieFinder [24] adopts a ranked-list visualization and, by clicking on items, the user can see the recommendation criteria they meet. Moreover, IntersectionExplorer [8] uses the UpSet matrix [20] to visualize the number of common suggestions provided by the recommenders.…”

Section: Research About Faceted Information Explorationmentioning

confidence: 99%

Faceted Exploration of Cultural Heritage

Mauro¹,

Izzi²,

Pellegrino³

et al. 2020

Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

The richness of Cultural Heritage (CH) sites exposes tourists to an information overload which makes it difficult to efficiently select the items that they like and can practically visit within a tour.Faceted information exploration has been proposed as a solution to analyze large sets of data. However, most works focus on the inspection of a single type of information, e.g., hotels or music. In contrast, CH items are heterogeneous: they include natural and artificial monuments and different types of artworks which might be visited within a single tour. Moreover, CH sites are often visited in group, thus raising the expectation that all the involved people share information and decisions about what to do.In order to address this issue, we propose a map-based faceted exploration model that makes it possible to create custom, long-lasting maps representing a shared information space for user collaboration, and temporally project these maps on the basis of fine-grained filters which help users focus on items associated to short-term, specific interests. Our model supports the user in the organization and filtering of CH information on the basis of multiple perspectives related to the attributes of items. We propose graphical widgets to support interactive data visualization, faceted exploration, categorybased information hiding and transparency of results at the same time. The widgets are based on the sunburst diagram, which compactly displays visualization criteria on data categories by showing facets and facet values in a circular structure.

show abstract

“…The system allows the user to explore a music collection through latent affective dimensions, thereby improving acceptance and understanding of recommendations. MyMovieMixer [47] is an interactive movie recommender that integrates different recommender techniques with interactive faceted filtering methods, called "blended recommending". The approach allows users to interact with a set of filter facets representing criteria that can serve as input for different recommendation methods, including collaborative and content-based filtering.…”

Section: Visualizationmentioning

confidence: 99%

IntersectionExplorer, a multi-perspective approach for exploring recommendations

Cardoso

Sedrakyan

Gutiérrez

et al. 2019

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

In this paper, we advent a novel approach to foster exploration of recommendations: Inter-sectionExplorer, a scalable visualization that interleaves the output of several recommender engines with human-generated data, such as user bookmarks and tags, as a basis to increase exploration and thereby enhance the potential to find relevant items. We evaluated the viability of IntersectionExplorer in the context of conference paper recommendation, through three user studies performed in different settings to understand the usefulness of the tool for diverse audiences and scenarios. We analyzed several dimensions of user experience and other, more objective, measures of performance. Results indicate that users found Intersec-tionExplorer to be a relatively fast and effortless tool to navigate through conference papers. Objective measures of performance linked to interaction showed that users were not only interested in exploring combinations of machine-produced recommendations with bookmarks of users and tags, but also that this "augmentation" actually resulted in increased likelihood of finding relevant papers in explorations. Overall, the findings suggest the viability of In-tersectionExplorer as an effective tool, and indicate that its multi-perspective approach to exploring recommendations has great promise as a way of addressing the complex humanrecommender system interaction problem.

show abstract

Blended Recommending

Cited by 42 publications

References 36 publications

A Service-oriented Perspective on the Summarization of Recommendations: Preliminary Experiment

A Service-oriented Perspective on the Summarization of Recommendations: Preliminary Experiment

Faceted Exploration of Cultural Heritage

IntersectionExplorer, a multi-perspective approach for exploring recommendations

Contact Info

Product

Resources

About