In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change. With a foundation in AI and data analytics, Recommender Systems can be effective in various domains, from e-commerce to energy management, inclusion and well-being. By harnessing the power of recommendation algorithms under a multi-stakeholder perspective, organizations and researchers can guide users towards more sustainable choices and behaviors, contributing to broader environmental and social goals. With this aim, our workshop provides a unique platform for researchers, practitioners, and platform owners to explore the integration of sustainability principles into Recommender Systems. Through presentations, discussions, and panels, participants can explore the theoretical foundations, practical implementations, and ethical and environmental considerations of sustainable Recommender Systems. By fostering collaboration and knowledge exchange, the workshop aims to catalyze innovation and inspire collective action towards a more sustainable future.

In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change. With a foundation in AI and data analytics, Recommender Systems can be effective in various domains, from e-commerce to energy management and well-being. By harnessing the power of recommendation algorithms under a holistic perspective, organizations and researchers can guide users towards more sustainable choices and behaviors, contributing to broader environmental and social goals. With this aim, our workshop provides a unique opportunity for researchers, practitioners, and stakeholders to explore the integration of sustainability principles into Recommender Systems. Through presentations, discussions, and panels, participants explore the theoretical foundations, practical implementations, and ethical and environmental issues of sustainable Recommender Systems. By fostering collaboration and knowledge exchange, the workshop aims to catalyze innovation and inspire collective action towards a more sustainable future.

When a multistakeholder recommender system (MRS) is designed to produce sustainable urban tourism promotion, two conflicting goals are of practical interest: (i) to cut down the number of visitors at popular sites and (ii) to satisfy tourists’ preferences, often biased towards popular sites. By modelling the tourists’ limited knowledge of the visited city — an important but often overlooked detail — we simulate interactions between tourists and an MRS that jointly optimises tourist’s utility and promotes less popular sites. Experiments based on data logs collected in three tourist cities reveal that such an MRS can lift tourist’s utility and at the same time reduce the number of visitors at popular sites, manifesting a so-called positive-sum impact. However, a delicate balance is crucial; under- or over-promotion of unpopular sites in the recommendation lists can be adverse to both destination and tourist’s utility.

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow, and the research must consider other important factors, particularly trustworthiness. A trustworthy recommender system (TRS) should not only be accurate but also transparent, unbiased, fair, and robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast-developing field of TRSs. To this end, in this article, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have achieved a great success and benefted the society. However, most of the research on RS has focused on the improvement of the recommendation accuracy, while ignoring other important qualities, such as trustworthiness (robustness, fairness, explainability, privacy and security) and social impact (influence on users' recognition and behaviours) of the recommendations. These are important aspects and cannot be overlooked since they measure properties that determine whether the recommendation service is reliable, trustworthy and benefcial to individual users and society. In this work, responsible recommendations refer to trustworthy recommendation techniques and positive-social-impact recommendation results.

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2022 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2021 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2020 workshop provides a venue for presenting and discussing approaches for the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in online environments.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2019 workshop provides a venue for presenting and discussing approaches for next generation of CARS and application domains that may require a variety of dimensions of contexts and cope with its dynamic properties.

User preferences in the form of absolute feedback, s.a., ratings, are widely exploited in Recommender Systems (RSs). Recent research has explored the usage of preferences expressed with pairwise comparisons, which signal relative feedback. It has been shown that pairwise comparisons can be effectively combined with ratings, but, it is important to fine tune the technique that leverages both types of feedback. Previous approaches train a single model by converting ratings into pairwise comparisons, and then use only that type of data. However, we claim that these two types of preferences reveal different information about users interests and should be exploited differently. Hence, in this work, we develop a ranking technique that separately exploits absolute and relative preferences in a hybrid model. In particular, we propose a joint loss function which is computed on both absolute and relative preferences of users. Our proposed ranking model uses pairwise comparisons data to predict the user's preference order between pairs of items and uses ratings to push high rated (relevant) items to the top of the ranking. Experimental results on three different data sets demonstrate that the proposed technique outperforms competitive baseline algorithms on popular ranking-oriented evaluation metrics.

Media monitoring services allow their customers, mostly companies, to receive, on a daily basis, a list of documents from mass media that discuss topics relevant to the company. However, media monitoring services often generate these lists by using keyword-filtering techniques, which introduce many false positives. Hence, before the end users, i.e., the employees of the company, may consult these lists and find relevant documents, a human editor must inspect the keyword-filtered documents and remove the false positives. This is a time consuming job. In this paper we present a recommender system that aims at reducing the number of documents that the editor needs to inspect every day. The proposed solution classifies documents (represented with TF-IDF and embeddings features) using techniques trained on data containing the editors’ past actions (i.e. the removals of false positives). The proposed technique is shown to be able to correctly predict the true positives, thus reducing the number of documents that the editor needs to inspect every day.

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice prediction that is based on a new form of weighting individuals’ preferences. The approach is based on network science, and, in particular, it relies on the computation of node centrality scores in preferences similarity networks of groups. We also motivate and introduce a non-linear (exponential) remapping of the individuals’ preferences. Based on offline experiments we demonstrate: 1) non-linear remapping of preferences is useful to better predict group choices and generate recommendations; and 2) our weighted approach predicts the actual group choices more accurately than current state-of-the-art methods for group recommendations.

Preference data in the form of ratings or likes for items are widely used in many Recommender Systems. However, previous research has shown that even item comparisons, which generate pairwise preference data, can be used to model user preferences. Moreover, pairwise preferences can be effectively combined with ratings to compute recommendations. In such hybrid approaches, the Recommender System requires to elicit both types of preference data from the user. In this work, we aim at identifying how and when to elicit pairwise preferences, i.e., when this form of user preference data is more meaningful for the user to express and more beneficial for the system. We conducted an online A/B test and compared a rating-only based system variant with another variant that allows the user to enter both types of preferences. Our results demonstrate that pairwise preferences are valuable and useful, especially when the user is focusing on a specific type of items. By incorporating pairwise preferences, the system can generate better recommendations than a state of the art rating-only based solution. Additionally, our results indicate that there seems to be a dependency between the user's personality, the perceived system usability and the satisfaction for the preference elicitation procedure, which varies if only ratings or a combination of ratings and pairwise preferences are elicited.

Online platforms which assist people in finding a suitable partner or match, such as online dating and job recruiting environments, have become increasingly popular in the last decade. Many of these platforms include recommender systems which aim at helping users discover other people who will also be interested in them. These recommender systems benefit from contemplating the interest of both sides of the recommended match, however the question of how to optimally balance the interest and the response of both sides remains open. In this study we present a novel recommendation method for recommending people to people. For each user receiving a recommendation, our method finds the optimal balance of two criteria: a) the likelihood of the user accepting the recommendation; and b) the likelihood of the recommended user positively responding. We extensively evaluate our recommendation method in a group of active users of an operational online dating site. We find that our method is significantly more effective in increasing the number of successful interactions compared to a state-of-the-art recommendation method.

Recommender Systems (RSs) are commonly used in web applications to support users in finding items of their interest. In this paper we propose a novel RS approach that supports human decision making by leveraging data acquired in the physical world. We consider a scenario in which users' choices to visit points of interests (POIs) are tracked and used to generate recommendations for not yet visited POIs. We propose a novel approach to user behaviour modelling that is based on Inverse Reinforcement Learning (IRL). Two recommendation strategies based on the proposed behaviour model are also proposed; they generate recommendations that differ from the common approach based on user next action prediction. Our experimental analysis shows that the proposed approach outperforms state of the art models in terms of the overall utility the user gains by following the provided recommendations and the novelty of the recommended items.

We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user is presented with two music excerpts. After listening to both, the user provides a pairwise score (i.e. which of the two items is preferred) for each pair of music excerpts. The novelty of the demo is the integration of the unobtrusive acquisition of facial expressions through the webcam. During the listening of the music excerpts, the system extracts features related to the facial expressions of the user several times per second. The interaction runs as a web application, which allows for a large-scale remote acquisition of emotional data. Up to now, such acquisitions were usually done in controlled environments with few subjects, hence being of little use for the recommender systems community.

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.

Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building recommendations. In our scenario, users provide pairwise preference scores for a set of item pairs, indicating how much one item in each pair is preferred to the other. We propose a matrix factorization (MF) and a nearest neighbor (NN) prediction techniques for pairwise preference scores. Our MF solution maps users and items pairs to a joint latent features vector space, while the proposed NN algorithm leverages specific user-to-user similarity functions well suited for comparing users preferences of that type. We compare our approaches to state of the art solutions and show that our solutions produce more accurate pairwise preferences and ranking predictions.

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.

Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.

Query revisions in a conversational system can be efficiently computed by assuming that the profiles of the potential users are in a predefined, a priori known and finite set. However, without any additional knowledge of the actual profiles distribution, the system may miss the true profiles of the users, hence deteriorating the system performance. We propose a method for identifying a tailored set of profiles that is acquired by analysing the implicitly shown preferences of the users that interacted with the system. We show that with the proposed method the system can efficiently identify good query revisions.

A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics from core algorithmic issues to the management of the human computer interaction.

Context-Aware Recommender Systems locally adapt to a specific contextual situation the rating prediction computed by a traditional context-free recommender. In this paper we present a novel semantic pre-filtering approach that can be tuned to the optimal level of contextualization by aggregating contextual situations that are similar to the target one. The similarities of contextual situations are derived from the available contextually tagged users' ratings according to how similarly the contextual conditions influence the user's rating behavior. We present an extensive evaluation of the performance of our pre-filtering approach on several data sets, showing that it outperforms state-of-the-art context-aware Matrix Factorization approaches.

We propose a novel approach to context-aware music recommendation - recommending music suited for places of interest (POIs). The suggested hybrid approach combines two techniques -- one based on representing both POIs and music with tags, and the other based on the knowledge of the semantic relations between the two types of items. We show that our approach can be scaled up using a novel music auto-tagging technique and we compare it in a live user study to: two non-hybrid solutions, either based on tags or on semantic relations; and to a context-free but personalized recommendation approach. In the considered scenario, i.e., a situation defined by a context (the POI), we show that personalization (via music preference) is not sufficient and it is important to implement effective adaptation techniques to the user's context. In fact, we show that the users are more satisfied with the recommendations generated by combining the tag-based and knowledge-based context adaptation techniques, which exploit orthogonal types of relations between places and music tracks.

The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor-quality data during training, that is, garbage in, garbage out. Active learning aims to remedy this problem by focusing on obtaining better-quality data that more aptly reflects a user's preferences. However, traditional evaluation of active learning strategies has two major flaws, which have significant negative ramifications on accurately evaluating the system's performance (prediction error, precision, and quantity of elicited ratings). (1) Performance has been evaluated for each user independently (ignoring system-wide improvements). (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). In this article we show that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system centric). We propose a new evaluation methodology and use it to evaluate some novel and state-of-the-art rating elicitation strategies. We found that the system-wide effectiveness of a rating elicitation strategy depends on the stage of the rating elicitation process, and on the evaluation measures (MAE, NDCG, and Precision). In particular, we show that using some common user-centric strategies may actually degrade the overall performance of a system. Finally, we show that the performance of many common active learning strategies changes significantly when evaluated concurrently with the natural acquisition of ratings in recommender systems.

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 ([email protected]) focuses on approaches for supporting effective and efficient human decision making in different types of recommendation scenarios.

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.

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.

Day hospital workflows are highly dynamic. It is, therefore, important to provide patients with timely information about their next activity, where it takes place, and when it starts. In this paper we present MobiDay, a novel mobile service integrated in the hospital information system that supports patients and clinicians in a day hospital scenario. We describe the MobiDay message-posting algorithm that uses context-aware rules provided by clinicians to decide the time and content of the guidance messages sent to the patient's device. MobiDay was tested with real patients during a 4-months-long experiment held in the hospital of Meran in South Tyrol, Italy. Here we report on the system evaluation results. Moreover, we discuss the pros and cons of MobiDay design choices and propose some general guidelines for the development of effective message-based mobile guidance services for patients.

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.

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.

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.

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 counterproposals. We describe a mobile application implementing the proposed approach and its evaluation in a live user experiment.

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.

No abstract available.

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. Copyright 2009 ACM.

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.

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.

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.

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.

This article presents a decision-maker model, called learning automaton, exhibiting adaptive behavior in highly uncertain stochastic environments. This learning model is used in solving constraint satisfaction problems (CSPs) by a procedure that can be viewed as hill climbing in probability space. the use of a fast learning algorithm that relaxes previous common assumptions is investigated. It is proven that the algorithm converges with probability 1 to a solution of the CSP and a set of test problems show that good performance can be achieved. In particular, it is shown that this method achieves a higher level of performance than that presented in a previous similar approach. Finally, it is estimated the speedup of a parallel implementation and the proposed algorithm is compared with a backtracking algorithm enhanced with standard CSP techniques. © 1994 John Wiley & Sons, Inc.