Publication statistics

Pub. period:2003-2012
Pub. count:25
Number of co-authors:32



Co-authors

Number of publications with 3 favourite co-authors:

Linas Baltrunas:5
Shlomo Berkovsky:3
Tsvi Kuflik:3

 

 

Productive colleagues

Francesco Ricci's 3 most productive colleagues in number of publications:

Loren Terveen:69
Shlomo Berkovsky:25
Tsvi Kuflik:23
 
 
 

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Francesco Ricci

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Publications by Francesco Ricci (bibliography)

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2012
 
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Moling, Omar, Baltrunas, Linas and Ricci, Francesco (2012): Optimal radio channel recommendations with explicit and implicit feedback. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 75-82.

The very large majority of recommender systems are running as server-side applications, and they are controlled by the content provider, i.e., who provides the recommended items. This paper focuses on a different scenario: the user is supposed to be able to access content from multiple providers, in our application they offer radio channels, and it is up to a personal recommender installed on the clients' side to decide which channel to select and recommend to the user. We exploit the implicit feedback derived from the user's listening behavior, and we model channel recommendation as a sequential decision making problem. We have implemented a personal RS that integrates reinforcement learning techniques to decide what channel to play every time the user asks for a new music track or the current track finishes playing. In a live user study we show that the proposed system can sequentially select the next channel to play such that the users listen to the streamed tracks for a larger fraction, and for more time, compared to a baseline system not exploiting implicit feedback.

© All rights reserved Moling et al. and/or ACM Press

 
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Gemmis, Marco de, Felfernig, Alexander, Lops, Pasquale, Ricci, Francesco, Semeraro, Giovanni and Willemsen, Martijn C. (2012): RecSys'12 workshop on human decision making in recommender systems. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 347-348.

Interacting with a recommender system means to take different decisions such as selecting an item from a recommendation list, selecting a specific item feature value (e.g., camera's size, zoom) as a search criteria, selecting feedback features to be critiqued in a critiquing based recommendation session, or selecting a repair proposal for inconsistent user preferences when interacting with a knowledge-based recommender. In all these situations, users face a decision task. This workshop (Decisions@RecSys) focuses on approaches for supporting effective and efficient human decision making in different types of recommendation scenarios.

© All rights reserved Gemmis et al. and/or ACM Press

 
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Ludwig, Bernd, Ricci, Francesco and Yumak, Zerrin (2012): 1st workshop on recommendation technologies for lifestyle change 2012. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 357-358.

The workshop on Recommendation Technologies for Lifestyle Change will be an opportunity for discussing open issues, and propose technical solutions for the designing of intelligent information systems that can support and promote lifestyle change. The objective of these systems is to provide users with up-to-date information, and help them to make choices in every day life activities establishing a sustainable compromise between quality of life, individuality, and fun.

© All rights reserved Ludwig et al. and/or ACM Press

2011
 
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Braunhofer, Matthias, Kaminskas, Marius and Ricci, Francesco (2011): Recommending music for places of interest in a mobile travel guide. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 253-256.

Context-aware music recommender systems suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context-aware recommendation task: selecting music suited for a place of interest (POI), which the user is visiting, and that is illustrated in a mobile travel guide. We have designed an approach for this novel recommendation task by matching music to POIs using emotional tags. In order to test our approach, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI. The results of the study show that users judge the recommended music suited for the POIs, and the music is rated higher when it is played in this usage scenario.

© All rights reserved Braunhofer et al. and/or ACM Press

 
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Guzzi, Francesca, Ricci, Francesco and Burke, Robin (2011): Interactive multi-party critiquing for group recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 265-268.

Group recommender systems (RS) are used to support groups in making common decisions when considering a set of alternatives. Current approaches generate group recommendations based on the users' individual preferences models. We believe that members of a group can reach an agreement more effectively by exchanging proposals suggested by a conventional RS. We propose to use a critiquing RS that has been shown to be effective in single-user recommendation. In the group recommendation context, critiquing allows each user to get new recommendations similar to the proposals made by the other group members and to communicate the rationale behind their own counter-proposals. We describe a mobile application implementing the proposed approach and its evaluation in a live user experiment.

© All rights reserved Guzzi et al. and/or ACM Press

 
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Baltrunas, Linas, Ludwig, Bernd and Ricci, Francesco (2011): Matrix factorization techniques for context aware recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 301-304.

Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.

© All rights reserved Baltrunas et al. and/or ACM Press

 
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Adomavicius, Gediminas, Baltrunas, Linas, Hussein, Tim, Ricci, Francesco and Tuzhilin, Alexander (2011): 3rd workshop on context-aware recommender systems (CARS 2011). In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 379-380.

CARS 2011 builds upon the success of the two previous editions held in conjunction with the 3rd and 4th ACM Conferences on Recommender Systems in 2009 and 2010. The first CARS Workshop was held in New York, NY, USA (2009), and Barcelona, Spain, was the home of the second CARS Workshop in 2010.

© All rights reserved Adomavicius et al. and/or ACM Press

 
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Kaminskas, Marius and Ricci, Francesco (2011): Location-Adapted Music Recommendation Using Tags. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization 2011. pp. 183-194.

Context-aware music recommender systems are capable to suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context aware recommendation task -- selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags attached by a users' population to both music and POIs. Moreover, we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. In order to test our hypothesis, i.e., that the users will reckon that a music track suits a POI when this track is selected by our approach, we have designed a live user experiment where subjects are repeatedly presented with POIs and a selection of music tracks, some of them matching the presented POI and some not. The results of the experiment show that there is a strong overlap between the users' selections and the best matching music that is recommended by the system for a POI.

© All rights reserved Kaminskas and Ricci and/or their publisher

2010
 
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Zanker, Markus, Ricci, Francesco, Jannach, Dietmar and Terveen, Loren (2010): Measuring the impact of personalization and recommendation on user behaviour. In International Journal of Human-Computer Studies, 68 (8) pp. 469-471.

 
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Baltrunas, Linas, Makcinskas, Tadas and Ricci, Francesco (2010): Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 2010 ACM Conference on Recommender Systems 2010. pp. 119-126.

The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members. This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.

© All rights reserved Baltrunas et al. and/or ACM Press

2009
 
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Mahmood, Tariq and Ricci, Francesco (2009): Improving recommender systems with adaptive conversational strategies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia 2009. pp. 73-82.

Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.

© All rights reserved Mahmood and Ricci and/or their publisher

 
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Baltrunas, Linas and Ricci, Francesco (2009): Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the 2009 ACM Conference on Recommender Systems 2009. pp. 245-248.

Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users' ratings for items. It assumes that the users' previously recorded ratings can help in predicting future ratings. This has been validated extensively, but in some domains item ratings can be influenced by contextual conditions, such as the time or the goal of the item consumption. This type of information is not exploited by standard CF models. This paper introduces and analyzes a novel pre-filtering technique for context-aware CF called item splitting. In this approach, the ratings of certain items are split, according to the value of an item-dependent contextual condition. Each split item generates two fictitious items that are used in the prediction algorithm instead of the original one. We evaluated this approach on real world and semi-synthetic data sets using matrix-factorization and nearest neighbor CF algorithms. We show that item splitting can be beneficial and its performance depends on the item selection method and on the influence of the contextual variables on the item ratings.

© All rights reserved Baltrunas and Ricci and/or ACM Press

 
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Adomavicius, Gediminas and Ricci, Francesco (2009): RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009). In: Proceedings of the 2009 ACM Conference on Recommender Systems 2009. pp. 423-424.

2008
 
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Nguyen, Quang Nhat and Ricci, Francesco (2008): Long-term and session-specific user preferences in a mobile recommender system. In: Proceedings of the 2008 International Conference on Intelligent User Interfaces 2008. pp. 381-384.

User preferences acquisition plays a very important role for recommender systems. In a previous paper, we proposed a critique-based mobile recommendation methodology exploiting both long-term and session-specific user preferences. In this paper, we evaluate the impact on the recommendation accuracy of the two kinds of user preferences. We have ran off-line experiments exploiting the log data recorded in a previous live-user evaluation, and we show here that exploiting both long-term and session-specific preferences results in a better recommendation accuracy than using a single user model component. Moreover, we show that when the simulated user behavior deviates from that dictated by the acquired user model the session-specific preferences are more useful than the long-term ones in predicting user decisions.

© All rights reserved Nguyen and Ricci and/or ACM Press

 
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Mahmood, Tariq and Ricci, Francesco (2008): Adapting the interaction state model in conversational recommender systems. In: Fensel, Dieter and Werthner, Hannes (eds.) Proceedings of the 10th International Conference on Electronic Commerce - ICEC 2008 August 19-22, 2008, Innsbruck, Austria. p. 33.

2007
 
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Mahmood, Tariq and Ricci, Francesco (2007): Learning and adaptivity in interactive recommender systems. In: Gini, Maria L., Kauffman, Robert J., Sarppo, Donna, Dellarocas, Chrysanthos and Dignum, Frank (eds.) Proceedings of the 9th International Conference on Electronic Commerce - ICEC 2007 August 19-22, 2007, Minneapolis, MN, USA. pp. 75-84.

 
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Berkovsky, Shlomo, Kuflik, Tsvi and Ricci, Francesco (2007): Cross-Domain Mediation in Collaborative Filtering. In: Conati, Cristina, McCoy, Kathleen F. and Paliouras, Georgios (eds.) User Modeling 2007 - 11th International Conference - UM 2007 June 25-29, 2007, Corfu, Greece. pp. 355-359.

 
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Werthner, Hannes, Hansen, Hans Robert and Ricci, Francesco (2007): Recommender Systems. In: HICSS 2007 - 40th Hawaii International International Conference on Systems Science 3-6 January, 2007, Waikoloa, Big Island, HI, USA. p. 167.

 
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Berkovsky, Shlomo, Eytani, Yaniv, Kuflik, Tsvi and Ricci, Francesco (2007): Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 9-16.

Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users.

© All rights reserved Berkovsky et al. and/or ACM Press

 
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Berkovsky, Shlomo, Kuflik, Tsvi and Ricci, Francesco (2007): Distributed collaborative filtering with domain specialization. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 33-40.

User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.

© All rights reserved Berkovsky et al. and/or ACM Press

 
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Bridge, Derek and Ricci, Francesco (2007): Supporting product selection with query editing recommendations. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 65-72.

Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user's actions; infer constraints on the user's utility function and add them to a user model; use the constraints to deduce which queries the user is likely to try next; and advise the user to avoid those that are unsatisfiable. We call this information recommendation. We give a detailed formulation of information recommendation for the case of products that are described by a set of Boolean features. Our experimental results show that if the user is given advice, the number of queries she needs to try before finding the product of highest utility is greatly reduced. We also show that an advisor that confines its advice to queries that the user model predicts are likely to be tried next will give shorter advice than one whose advice is unconstrained by the user model.

© All rights reserved Bridge and Ricci and/or ACM Press

 
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Nguyen, Quang Nhat and Ricci, Francesco (2007): Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 81-88.

Supporting conversational approaches in mobile recommender systems is challenging because of the inherent limitations of mobile devices and the dependence of produced recommendations on the context. In a previous work, we proposed a critique-based mobile recommendation approach and presented the results of a live users evaluation. Live-user evaluations are expensive and there we could not compare different system variants to check all our research hypotheses. In this paper, we present an innovative simulation methodology and its use in the comparison of different user-query representation approaches. Our simulation test procedure replays off-line, against different system variants, interactions recorded in the live-user evaluation. The results of the simulation tests show that the composite query representation, which employs both logical and similarity queries, does improve the recommendation performance over a representation using either a logical or a similarity query.

© All rights reserved Nguyen and Ricci and/or ACM Press

2005
 
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Wietsma, Rene T. A. and Ricci, Francesco (2005): Product Reviews in Mobile Decision Aid Systems. In: Rukzio, Enrico, Hkkil, Jonna, Spasojevic, Mirjana, Mntyjrvi, Jani and Ravi, Nishkam (eds.) PERMID 2005 - Pervasive Mobile Interaction Devices - Mobile Devices as Pervasive User Interfaces and Interaction Devices - Workshop in conjunction with The 3rd International Conference on Pervasive Computing PERVASIVE 2005 May 11, 2005, Munich, Germany. pp. 15-18.

2004
 
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Werthner, Hannes and Ricci, Francesco (2004): E-commerce and tourism. In Communications of the ACM, 47 (12) pp. 101-105.

2003
 
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Cavada, Dario, Mirzadeh, Nader, Ricci, Francesco and Venturini, Adriano (2003): Interactive Trip Planning with Trip@dvise. In: Proceedings of IFIP INTERACT03: Human-Computer Interaction 2003, Zurich, Switzerland. p. 1105.

 
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Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/francesco_ricci.html

Publication statistics

Pub. period:2003-2012
Pub. count:25
Number of co-authors:32



Co-authors

Number of publications with 3 favourite co-authors:

Linas Baltrunas:5
Shlomo Berkovsky:3
Tsvi Kuflik:3

 

 

Productive colleagues

Francesco Ricci's 3 most productive colleagues in number of publications:

Loren Terveen:69
Shlomo Berkovsky:25
Tsvi Kuflik:23
 
 
 

Upcoming Courses

go to course
Design Thinking: The Beginner's Guide
Starts tomorrow LAST CALL!
go to course
The Psychology of Online Sales: The Beginner's Guide
Starts the day after tomorrow !
 
 

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !

 
 

Our Latest Books

 
 
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
start reading
 
 
 
 
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
start reading