Personalized search and recommendation are increasingly powered by large language models (LLMs) and multimodal deep learning, yet these advances raise practical concerns about bias, unfair outcomes, privacy risks, and limited transparency . At the same time, personalization pipelines are increasingly end-to-end and adaptive, meaning that small modeling or logging choices can propagate into downstream exposure effects and feedback loops that are hard to detect post hoc. As conversational and generative interfaces become the default access layer, users also face new challenges in judging provenance, uncertainty, and whether recommendations reflect their intent or the model's priors. This tutorial provides a unified, retrieval-centric view of trustworthy personalization: (1) how to measure and mitigate unfairness and bias in ranking, clustering, and classification; (2) how to design bias-aware retrieval experiences that improve user awareness; (3) how to support privacy-aware personal information retrieval (e.g., archived email, lifelogs) without sacrificing utility; and (4) how these concerns evolve for multimodal and retrieval-augmented generative recommendation, including LLM-based retriever/reranker settings. Participants will leave with concrete evaluation protocols, common failure modes, and a practical blueprint for building and auditing end-to-end IR/Rec pipelines for effectiveness, fairness, and privacy.

Despite the importance of context in Recommender Systems (RSs) more generally, and its clear applicability in the food domain, most existing research focuses on single contextual factors, and only considers simple extrinsic factors such as location and time. No RSs research has systematically explored the impact of multiple dynamic factors, or investigated the effect of emotion in determining people’s eating, recipe rating and nutritional intake behaviour. To bridge these gaps, we conducted a comprehensive large-scale (n=397) crowdsourced experimental study to uncover the intricate relationship between various simulated contextual factors and users’ subsequent recipe rating and implied nutritional intake behaviour. We further aimed to explore how these contextual factors can be incorporated to improve recommendation performance. Four distinct types of contextual factors were investigated: seasonal, emotional, busyness and physical activity, encompassing a total of seven elements. Our findings show that people’s eating preferences and the likelihood of them choosing to eat healthy recipes vary depending on the simulated context they find themselves in. Moreover, we demonstrate how these contextual features can be used to significantly improve recipe rating prediction performance. Our research has implications for the future development of food RSs, and shows that emotion-aware systems could lead to better healthy food 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.

It is well-established that a sense of belonging is important for students' academic success. This is especially true for international students in introductory programming courses. In higher education, students' sense of belonging is closely associated with their interest in the subject and related career. This study explores the use of a narrative-based gamification approach to enhance students' sense of belonging in introductory programming courses. The gamified design was piloted with 32 Chinese students enrolled in a big data analytic course using Python at a British university. Results showed that the narrative-based gamified approach greatly improved students' sense of belonging in terms of peer relationships and related-ness to the subject, and increased their learning motivation, career interest, class engagement, and learning retention.

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the "picture" of the Covid-19 pandemic accessed by users differs. We explore variations in what users "see" concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation - users' information needs, their geo-locations and query languages - and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse "picture" of the pandemic seen through Google.

Due to increasing possibilities to create digital video, we are facing the emergence of large video archives that are made accessible either online or offline. Though a lot of research has been spent on video retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal. At the same time, the organization of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualizing this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a heterogeneous personal digital libraries of rich multimedia and sensory content for every individual is becoming a reality.

Recommender System research has evolved to focus on developing algorithms capable of high performance in online systems. This development calls for a new evaluation infrastructure that supports multi-dimensional evaluation of recommender systems. Today's researchers should analyze algorithms with respect to a variety of aspects including predictive performance and scalability. Researchers need to subject algorithms to realistic conditions in online A/B tests. We introduce two resources supporting such evaluation methodologies: the new data set of stream recommendation interactions released for CLEF NewsREEL 2017, and the new Open Recommendation Platform (ORP). The data set allows researchers to study a stream recommendation problem closely by "replaying" it locally, and ORP makes it possible to take this evaluation "live" in a living lab scenario. Specifically, ORP allows researchers to deploy their algorithms in a live stream to carry out A/B tests. To our knowledge, NewsREEL is the first online news recommender system resource to be put at the disposal of the research community. In order to encourage others to develop comparable resources for a wide range of domains, we present a list of practical lessons learned in the development of the dataset and ORP.

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.

Test collections have a long history of supporting repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists for IR systems that are designed to index and retrieve multimodal lifelog data. In this paper we introduce the first test collection for personal lifelog data, which has been employed for the NTCIR12-Lifelog task. In this paper, the requirements for the test collection are motivated, the process of creating the test collection is described, along with an overview of the test collection. Finally suggestions are given for possible applications of the test collection.

Stronger engagement and greater participation is often crucial to reach a goal or to solve an issue. Issues like the emerging employee engagement crisis, insufficient knowledge sharing, and chronic procrastination. In many cases we need and search for tools to beat procrastination or to change people's habits. Gamification is the approach to learn from often fun, creative and engaging games. In principle, it is about understanding games and applying game design elements in a non-gaming environments. This offers possibilities for wide area improvements. For example more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR) it is not hard to imagine that many tasks could benefit from gamification techniques. Besides several manual annotation tasks of data sets for IR research, user participation is important in order to gather implicit or even explicit feedback to feed the algorithms. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy. Given the enormous response to the first and second GamifIR workshops that were both co-located with ECIR, and the broad range of topics discussed, we now organized the third workshop at SIGIR 2016 to address a range of emerging challenges and opportunities.

With an increasing amount of video data in our daily life, the need for content-based search in videos increases as well. Though a lot of research has been spent on video retrieval tools and methods which allow for automatic search in videos through content-based queries, still the performance of automatic video retrieval is far from optimal. In this tutorial we discussed (i) proposed solutions for improved video content navigation, (ii) typical interaction of content-based querying features, and (iii) advanced video content visualization methods. Moreover, we discussed interactive video search systems and ways to evaluate their performance.

We present a browser-based visualization tool that allows users to explore movies and online videos based on the violence level of these videos. The system offers visualizations of annotations and results of the MediaEval 2012 Affect Task and can interactively download and analyze content from video hosting sites like YouTube.

Detecting violent scenes in movies is an important video content understanding functionality e.g., for providing automated youth protection services. One key issue in designing algorithms for violence detection is the choice of discriminative features. In this paper, we employ mid-level audio features and compare their discriminative power against low-level audio and visual features. We fuse these mid-level audio cues with low-level visual ones at the decision level in order to further improve the performance of violence detection. We use Mel-Frequency Cepstral Coefficients (MFCC) as audio and average motion as visual features. In order to learn a violence model, we choose two-class support vector machines (SVMs). Our experimental results on detecting violent video shots in Hollywood movies show that mid-level audio features are more discriminative and provide more precise results than low-level ones. The detection performance is further enhanced by fusing the mid-level audio cues with low-level visual ones using an SVM-based decision fusion.

Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating itemcollections, and highly sparse user profiles. This workshop (NRS'[email protected]) brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems. Additionally, we offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.

Evaluating adaptive and personalized information retrieval tech-niques is known to be a difficult endeavor. The rapid evolution of novel technologies in this scope raises additional challenges that further stress the need for new evaluation approaches and method-ologies. The BARS 2013 workshop seeks to provide a specific venue for work on novel, personalization-centric benchmarking approaches to evaluate adaptive retrieval and recommender systems.

In this demo paper, we describe our user-generated video search system, compromising of an iPad interface communicating with a remote server. The goal of this system is to provide an easy access to video content lacking textual annotations by clustering key frames. Moreover, the graphical user interface allows users to filter video content based on various semantic concepts.

We present a model for exploiting community-based usage information for video retrieval, where implicit usage information from past users is exploited in order to provide enhanced assistance in video retrieval tasks, and alleviate the effects of the semantic gap problem. We propose a graph-based model for all types of implicit and explicit feedback, in which the relevant usage information is represented. Our model is designed to capture the complex interactions of a user with an interactive video retrieval system, including the representation of sequences of user-system interaction during a search session. Building upon this model, four recommendation strategies are defined and evaluated. An evaluation strategy is proposed based on simulated user actions, which enables the evaluation of our recommendation strategies over a usage information pool obtained from 24 users performing four different TRECVid tasks. Furthermore, the proposed simulation approach is used to simulate usage information pools with different characteristics, with which the recommendation approaches are further evaluated on a larger set of tasks, and their performance is studied with respect to the scalability and quality of the available implicit information.

In this paper, we present a user study on a novel video search interface based on the concept of aspect browsing. We aim to confirm whether automatically suggesting new aspects can increase the performance of an aspect-based browser. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. We use a clustering technique to identify potential aspects and use the results to propose suggestions to the user to help them in their search task. We evaluate this approach by analysing the users' perception and by exploiting the log files.

In this paper we present an innovative approach for aiding users in the difficult task of video search. We use community based feedback mined from the interactions of previous users of our video search system to aid users in their search tasks. This feedback is the basis for providing recommendations to users of our video retrieval system. The ultimate goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. In particular we wish to make the difficult task of search for video much easier for users. The results of a user evaluation indicate that we achieved our goals, the performance of the users in retrieving relevant videos improved, and users were able to explore the collection to a greater extent.

So far, several approaches have been studied to bridge the problem of the Semantic Gap, the bottleneck in image and video retrieval. However, no approach is successful enough to increase retrieval performances significantly. One reason is the lack of understanding the user's interest, a major condition towards adapting results to a user. This is partly due to the lack of appropriate interfaces and the missing knowledge of how to interpret user's actions with these interfaces. In this paper, we propose to study the importance of various implicit indicators of relevance. Furthermore, we propose to investigate how this implicit feedback can be combined with static user profiles towards an adaptive video retrieval model.