Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users’ probable intent of using the service at a certain point in time into account. In recent years, researchers have thus started to address this challenge by incorporating intent-awareness into recommender systems. Correspondingly, a number of technical approaches were put forward, including diversification techniques, intent prediction models, or latent intent modeling approaches. In this article, we survey and categorize existing approaches to building the next generation of Intent-Aware Recommender Systems (IARS). Based on an analysis of current evaluation practices, we outline open gaps and possible future directions in this area, which in particular include the consideration of additional interaction signals and contextual information to further improve the effectiveness of such systems.

Recommender systems, though widely used, often struggle to engage users effectively. While deep learning methods have enhanced connections between users and items, they often neglect the user’s perspective. Knowledge-based approaches, utilizing knowledge graphs, offer semantic insights and address issues like knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. More recently, neural-symbolic systems, combining data-driven and symbolic techniques, show promise in recommendation systems, especially when used with knowledge graphs. Moreover, content features become vital in conversational recommender systems, which demand multi-turn dialogues. Recent literature highlights increasing interest in this area, particularly with the emergence of Large Language Models (LLMs), which excel in understanding user queries and generating recommendations in natural language. Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop aims to disseminate advancements and discuss about challenges and opportunities.

The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. This year, the workshop features a special challenge focused on ranking travel reviews. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.

Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these commonly applied approaches. Therefore, we propose to map the causality frameworks of potential outcomes and structural causal models onto the recommender systems domain in order to foster future research and development. For instance, applying causal discovery strategies on offline data to learn the causal graph in order to compute counterfactuals or improve debiasing strategies.

Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users and items, but they often miss a critical actor in the loop - the end-user. Knowledge-based approaches are gaining attention due to the availability of knowledge-graphs, such as DBpedia and Wikidata, which provide semantics-aware information on different knowledge domains. These approaches are being used for recommendation and challenges such as knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. Moreover, the emergence of neural-symbolic systems, which combine data-driven and symbolic methods, can significantly improve recommendation systems. A growing number of research papers on such topics demonstrate the growing interest and research potential of these systems. Furthermore, content features become crucial when interaction requires it. The development of conversational recommender systems presents new challenges, as they require multi-turn dialogues between users and systems, blurring the line between recommendation and retrieval. Evaluation of these systems goes beyond simple accuracy metrics and is hampered by the limited availability of datasets. While research and development into conversational recommender systems has been less prominent in the past, recent literature shows growing interest and potential for these systems.

The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Reproducibility is a main principle in science and fundamental to ensure scientific progress. However, many recent works point out that there are widespread deficiencies for this aspect in the AI field, making the reproducibility of results impractical or even impossible. We therefore studied the state of reproducibility support on the topic of Reinforcement Learning & Recommender Systems to analyse the situation in this context. We collected a total of 60 papers and analysed them by defining a set of variables to inspect the most important aspects that enable reproducibility, such as dataset, pre-processing code, hardware specifications, software dependencies, algorithm implementation, algorithm hyperparameters, and experiment code. Furthermore, we used the ACM Badges definitions assigning them to the selected papers. We discovered that, like in many other AI domains, the Reinforcement Learning & Recommender Systems field is grappling with a reproducibility crisis, as none of the selected papers were reproducible when strictly applying the ACM Badges definitions according to our analysis.

In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging. This is likely due to the massive proliferation of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language utterances. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they still remain at an early stage in terms of their recommendation capabilities via a conversation. In addition, we have been witnessing the advent of increasingly precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that may be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and neglect the huge amount of knowledge, both structured and unstructured, describing the domain of interest of a recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating explanations for recommended items. Knowledge-aware side information becomes crucial when a conversational interaction is implemented, in particular for preference elicitation, explanation, and critiquing steps.

The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Abstract There exist many research works that strive to answer the question “what news article is a user going to click next given his profile”. These works take into account the time dimension to reveal users’ preferences over time. However, few works exploit adequately the information that is hidden inside user sessions. User sessions include a list of user interactions with items within a short period of time such as 30 min, and can reveal her very last intentions. In this paper, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals. Thus, we are able to better capture the similarities among items that are co-selected inside a user session but also within any two consecutive sessions. We have evaluated experimentally our method and compare it against state-of-the-art algorithms on three real-life datasets. We demonstrate the superiority of our method over its competitors.

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.

The Workshop on Recommenders in Tourism (RecTour) 2021, which is held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Over the years, the Web has become a premier source of information in almost every area we can think about. When considering tourism, the Web became the primary source of information for travelers. When planning trips, people search for information about destinations, accommodations, attractions, means of transportation, in short, everything related to their future trip. Once done searching they reserve almost everything online. The blessing of the easily accessible information comes with the curse of information overload. This brings Web search techniques and recommendation systems come into play. This is especially true recently with the appearance of COVID-19 and the uncertainty and transformative power it brings to travelling. WebTour 2021 brings together researchers and practitioners working on developing and improving tools and techniques for improving users ability to better find relevant information that matches their needs.

The task “recommend a video to watch next?” has been in the focus of recommender systems’ research for a long time. However, adequately exploiting the clues hidden in the sequences of actions of user sessions in order to reveal users’ short-term intentions moved only recently into the focus of research. Based on a real-world application scenario, in this paper, we propose a Markov Chain-based transition probability matrix to efficiently reveal the short-term preferences of individuals. We experimentally evaluated our proposed method by comparing it against state-of-the-art algorithms in an offline as well as a live evaluation setting. In both cases our method not only demonstrated its superiority over its competitors, but exposed a clearly stronger engagement of users on the platform. In the online setting, our method improved the click-through rate by up to 93.61%. This paper therefore contributes real-world evidence for improving the recommendation effectiveness, by considering sequence-awareness, since capturing the short-term preferences of users is crucial in the light of items with a short life span such as tv programs (news, tv shows, etc.).

The present research explored to what extent user engagement in proactive recommendation scenarios is influenced by the accuracy of recommendations, concerns with information privacy, and trait personality. We hypothesized that people’s self-reported information privacy concerns would matter more when they received accurate (vs. inaccurate) proactive recommendations, because these pieces of advice would seem fair to them. We further hypothesized that this would particularly be the case for people high on the social personality trait Extraversion, who are by inclination prone to behaving in a more socially engaging manner. We put this to the test in a controlled experiment, in which users received manipulated proactive recommendations of high or low accuracy on their smartphone. Results indicated that information privacy concerns positively influenced a user’s engagement with proactive recommendations. Recommendation accuracy influenced user engagement in interaction with information privacy concerns and personality traits. Implications for the design of human-computer interaction for recommender systems are addressed.

The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submissions that we received. The topics of this year's workshop include context-aware recommendations, group recommender systems, hotel recommendations, destination characterization, next-POI recommendation, user interaction and experience, preference elicitation, user modeling and application of machine learning algorithms in the context of tourism recommender systems.

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest for the recommendation engine. The aim of knowledge-aware and conversational recommender systems is to go beyond the traditional accuracy goal and to start a new generation of algorithms and interactive approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis and exploitation of semi-structured textual sources.

The Workshop on Recommenders in Tourism (RecTour) 2018, which is held in conjunction with the 12th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this paper, we summarize our motivations to organize this workshop and give an overview of the submissions that we received. The topics of this year's workshop include points-of-interest (POI), hotel and airline recommendations, recommending composite items such as POI sequences, group recommender systems, context-aware recommendation, decision making, user interaction issues, explanations and evaluation of tourism recommenders.

This demo presents a conversational navigation approach for a diagnostic application of postharvest diseases of apple with the goal to educate users on the diagnosed diseases as well as to recommend consequences for the storage facility and what action to take for the next growing period. It thus builds on earlier works on picture-based navigation for conversational recommender systems and provides evidence for its usability based on a first small-scale comparative usability study.

rrecsys is a novel library in R for developing and assessing recommendation algorithms. In this demo, we extend rrecsys with functions for visual analytics of recommendation performance, that is one of the strong capabilities of the R environment. In particular, we show how the library can be used to depict dataset characteristics, train and test recommendation algorithms and to visually assess, for instance, their capability to exploit long-tail items for making correct predictions.

The deep learning wave is propagating through many research areas and communities. In the last years it quickly propagated to Recommendation Systems, a research area which aims to recommend items to users. Indeed, many deep learning models and architectures have been proposed for Recommendation Systems to improve collaborative filtering and content based algorithms. In this paper we propose a hybrid recommendation system combining user ratings and natural language text processing to solve the 0/1 recommendation problem. In particular, we describe a deep learning architecture combining two information sources, namely natural language text and user rating. Natural language text is used to learn a user-specific content-based classifier, while user ratings are used to develop user-adaptive collaborative filtering recommendations. We perform numerical experiments on MovieLens 1M and reach first preliminary, but promising results, showing the proposed architecture has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor.

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.

Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to select the best algorithm for field deployment. The goal of this work is therefore to compare the offline and the online evaluation methodology with the same study participants, i.e. a within users experimental design. This paper presents empirical evidence that the ranking of algorithms based on offline accuracy measurements clearly contradicts the results from the online study with the same set of users. Thus the external validity of the most commonly applied evaluation methodology is not guaranteed.

We focus on the problem of selecting reviewers (or raters) that are considered by a recommender system (or a user) under the aspect of security. Malicious reviewers can exert unreasonable influence, and can bias online consumers unfairly against an attacked item or competitor. This paper proposes an approach where a meta-recommender maximizes the aggregated diversity of reviewers when deciding which reviews should be considered by a recommender system or an online consumer. This problem can be of interest in many domains where producers or service providers may seek advantages by compromising competitors with fake reviews or ratings such as tourism and hospitality industries or even free open-source software. A solution is proposed for users linked in social networks, such as unstructured P2P societies. In order to evaluate the proposed solution, we describe a mechanism of selecting reviewers of software updates such that not all end-users of a software are impacted by a potentially malicious strict subset of all available reviewers, and we experimentally assess resistance to attacks.

Product configuration can be defined as the task of tailoring a product according to the specific needs of a customer. Due to the inherent complexity of this task, which for example includes the consideration of complex constraints or the automatic completion of partial configurations, various Artificial Intelligence techniques have been explored in the last decades to tackle such configuration problems. Most of the existing approaches adopt a single-site, centralized approach. In modern supply chain settings, however, the components of a customizable product may themselves be configurable, thus requiring a multisite, distributed approach. In this paper, we analyze the challenges of modeling and solving such distributed configuration problems and propose an approach based on Distributed Constraint Satisfaction. In particular, we advocate the use of Generative Constraint Satisfaction for knowledge modeling and show in an experimental evaluation that the use of generic constraints is particularly advantageous also in the distributed problem solving phase.

Recommender Systems (RS) help online customers in identifying those items from a variety of choices that best match their presumed needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and are capable of increasing the users' trust in the system's results. This paper presents results from an online experiment on a real-world platform indicating that explanations are an essential piece of functionality of a recommendation system, that significantly increases users' perception of the utility of a recommender system, the intention to use it repeatedly as well as the commitment to recommend it to others.

Recommender Systems (RS) serve online customers in identifying those items from a variety of choices that best match their needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and strongly increase the users' trust in the system's results. In this paper we propose a framework for generating knowledgeable explanations that exploits domain knowledge to transparently argue why a recommended item matches the user's preferences. Furthermore, results of an online experiment on a real-world platform show that users' perception of the usability of a recommender system is positively influenced by knowledgeable explanations and that consequently users' experience in interacting with the system, their intention to use it repeatedly as well as their commitment to recommend it to others are increased.

Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.