The digital era has made millions of manuscript images in Hebrew available to all. However, despite major advancements in handwritten text recognition over the past decade, an efficient pipeline for large scale and accurate conversion of these manuscripts into useful machine-readable form is still sorely lacking.We propose a pipeline that significantly improves recognition models for automatic transcription of Hebrew manuscripts. Transfer learning is used to fine-tune pretrained models. For post-recognition correction, it leverages text reuse, a common phenomenon in medieval manuscripts, and state-of-the-art large language models for medieval Hebrew.The framework successfully handles noisy transcriptions and consistently suggests alternate, better readings. Initial results show that word level accuracy increased by 10% for new readings proposed by text-reuse detection. Moreover, the character level accuracy improved by 18% by fine-tuning models on the first few pages of each manuscript.

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.

This workshop is dedicated to emphasizing the pivotal role of temporal dynamics in advancing recommender systems across various fields. While the significance of temporal factors in user behavior is widely acknowledged, effectively integrating these aspects into recommendation algorithms remains a complex challenge. In this workshop, we aim to showcase the application of temporal aspects in recommender systems across diverse domains such as healthcare, e-commerce, fashion, banking, travel, and film. We believe that this workshop will contribute to advancing temporal methodologies in recommender systems, ultimately leading to more precise recommendations.

Abstract Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

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.

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.

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 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.

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.

The workshop focus is on considering temporal aspects for recommender systems in general, regardless of the specific domain and application, trying to develop a holistic approach for dealing with temporal aspects in recommender systems, like personal assistants, news, tourism, health care, TV, e-commerce, social networks and so on.

The Workshop on Recommenders in Tourism (RecTour) 2017, which is held in conjunction with the eleventh Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this overview, we summarize our motivations to organize such a workshop and give an overview of the submissions that we received. The main topics discussed in this year's workshop include context-aware recommender systems, group recommender systems, and the impact of itinerary, sequence, and trust on travel-related recommendations.

Visitors to museums and other cultural heritage sites encounter a wealth of exhibits in a variety of subject areas, but can explore only a small number of them. Moreover, there typically exists rich complementary information that can be delivered to the visitor about exhibits of interest, but only a fraction of this information can be consumed during the limited time of the visit. Recommender systems may help visitors to cope with this information overload. Ideally, the recommender system of choice should model user preferences, as well as background knowledge about the museum's environment, considering aspects of physical and thematic relevancy. We propose a personalized graph‐based recommender framework, representing rating history and background multi‐facet information jointly as a relational graph. A random walk measure is applied to rank available complementary multimedia presentations by their relevancy to a visitor's profile, integrating the various dimensions. We report the results of experiments conducted using authentic data collected at the Hecht museum. An evaluation of multiple graph variants, compared with several popular and state‐of‐the‐art recommendation methods, indicates on advantages of the graph‐based approach.

In this paper, we summarize RecTour 2016 -- a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.

The tremendous popularity of Online Social Networks (OSN) has led to situations, where users have their profiles spread across multiple networks. These partial profiles reflect different user characteristics, depending mainly on the nature of the network, e.g., Facebook's social vs. LinkedIn's professional focus. Combining data gathered by multiple networks may benefit individual users, and the community as a whole, as this could facilitate the provision of more accurate services and recommendations. This paper reports on an exploratory study of the process of making such recommendations using a unique multi-network dataset containing user interests across multiple domains, e.g., music, books, and movies. We represent the data using a graph model and generate recommendations using a set of features extracted from and populated by the model. We assess the contribution of various network- and domain-related features to the accuracy of the recommendations and motivate future work into automated feature selection.

No abstract available.

No abstract available.

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.

Abstract Today, most document categorization in organizations is done manually. We save at work hundreds of files and e‐mail messages in folders every day. While automatic document categorization has been widely studied, much challenging research still remains to support user‐subjective categorization. This study evaluates and compares the application of self‐organizing maps (SOMs) and learning vector quantization (LVQ) with automatic document classification, using a set of documents from an organization, in a specific domain, manually classified by a domain expert. After running the SOM and LVQ we requested the user to reclassify documents that were misclassified by the system. Results show that despite the subjective nature of human categorization, automatic document categorization methods correlate well with subjective, personal categorization, and the LVQ method outperforms the SOM. The reclassification process revealed an interesting pattern: About 40% of the documents were classified according to their original categorization, about 35% according to the system's categorization (the users changed the original categorization), and the remainder received a different (new) categorization. Based on these results we conclude that automatic support for subjective categorization is feasible; however, an exact match is probably impossible due to the users' changing categorization behavior.

Abstract Web navigation enables easy access to vast amounts of information and services. However, it also poses a major risk to users' privacy. Various eavesdroppers constantly attempt to violate users' privacy by tracking their navigation activities and inferring their interests and needs (profiles). Users who wish to keep their intentions secret forego useful services to avoid exposure. The computer security community has concentrated on improving users' privacy by concealing their identity on the Web. However, users may want or need to identify themselves over the Net to receive certain services but still retain their interests, needs, and intentions in private. PRAW—a PRivAcy model for the Web suggested in this paper—is aimed at hiding users' navigation tracks to prevent eavesdroppers from inferring their profiles but still allowing them to be identified. PRAW is based on continuous generation of fake transactions in various fields of interests to confuse eavesdroppers' automated programs, thus providing them false data. A privacy measure is defined that reflects the difference between users' actual profile and the profile that eavesdroppers might infer. A prototype system was developed to examine PRAW's feasibility and conduct experiments to test its effectiveness. Encouraging results and their analysis are presented, as well as possible attacks and known limitations.

In information filtering (IF) systems, user long-term needs we expressed as user profiles. The quality of a user profile has a major impact on the performance of IF systems. The focus of the proposed research is on the study of user profile generation and update. The paper introduces methods for user profile generation, and proposes a research agenda for their comparison and evaluation.