Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users

In this paper, we consider the promise and challenges of deploying recommendation and information retrieval technology to help teachers locate resources for use in classroom instruction. The classroom setting is a complex environment presenting a number of challenges for recommendation, due to its inherent multi-stakeholder nature, the multiple objectives that quality educational resources and experiences must simultaneously satisfy, and potential disconnect between the direct user of the system and the end users of the resources it provides. In this paper, we outline these challenges, highlight opportunities for new research, and describe our work in progress in this area including insights from interviews with working teachers

FILTERING STRATEGY TO OVERCOME THE CLASSIC VIEW USING BI-OBJECTIVES

Within this paper, we suggested MobiContext, a hybrid cloud based Bi Objective Recommendation Framework (BORF) for mobile social systems. The MobiContext utilizes multi objective optimization strategies to generate personalized recommendations. Recently, recommendation systems have experienced significant evolution in the area of understanding engineering. The majority of the existing recommendation systems based their models on collaborative filtering approaches which make them easy to implement. However, performance of the majority of the existing collaborative filtering based recommendation system suffers because of the challenges, for example: (a) cold start, (b) data sparseness, and (c) scalability. Furthermore, recommendation issue is frequently characterized by the existence of many conflicting objectives or decision variables, for example users’ preferences and venue closeness. To deal with the problems relating to cold start and knowledge sparseness, the BORF performs data preprocessing using the Hub Average (HA) inference model. The outcomes of comprehensive experiments on the massive real dataset read the precision from the suggested recommendation framework. Furthermore, the Weighted Sum Approach (WSA) is implemented for scalar optimization as well as a transformative formula (NSGA II) is used for vector optimization to supply optimal tips to you in regards to a venue

User Curiosity Factor in Determining Serendipity of Recommender System

Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendipity. Serendipity can be described as an unexpected discovery that is useful. Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity is curiosity. While some researchers already addressed curiosity factor, it is found that the relationships between various serendipity component as perceived by the users and their curiosity levels is still yet to be researched. In this paper, the method to determine user curiosity model by considering the variation of rated items was presented, then relation to serendipity components using existing user feedback data was validated. The finding showed that the curiosity model was related to some user-perceived values of serendipity, but not all. Moreover, it also had positive effect on broadening the user preference.

AntRS: Recommending Lists through a Multi-Objective Ant Colony System

International audienceWhen people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms

From Music to Museum: Applications of Multi-Objective Ant Colony Systems to Real World Problems

International audienceRecommender systems are a flourishing domain in computer science for almost 30 years now. This rising popularity follows closely the number of data collected all around the world. Each and every internet user produces a huge amount of content during his lifetime. Recommender systems proactively help users to navigate these pieces of information by gathering, and selecting the items to users' needs. In this paper, we discuss the possibility and interest of applying our Multi-Objective Ant Colony System called AntRS to recommend items in different application domains. In particular, we show how our model performs better than the state-of-the-art models with music dataset, and describe our work-in-progress with the museum of fine arts in Nancy (France). The motivation behind this change of application domain is the recommendation of progressive sequences rather than unordered lists of items

A Critical Reexamination of Intra-List Distance and Dispersion

Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. Owing to their simplicity and flexibility, ILD and dispersion have been used in a plethora of diversified recommendation research. Nevertheless, we do not actually know what kind of items are preferred by them. We present a critical reexamination of ILD and dispersion from theoretical and experimental perspectives. Our theoretical results reveal that these objectives have potential drawbacks: ILD may select duplicate items that are very close to each other, whereas dispersion may overlook distant item pairs. As a competitor to ILD and dispersion, we design a diversity objective called Gaussian ILD, which can interpolate between ILD and dispersion by tuning the bandwidth parameter. We verify our theoretical results by experimental results using real-world data and confirm the extreme behavior of ILD and dispersion in practice.Comment: 10 pages, to appear in 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023

Evaluating Recommender Systems Qualitatively: A survey and Comparative Analysis

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsRecommender systems have improved users' online quality of life by helping them find interesting and valuable items within a large item set. Most recommender system validation research has focused on accuracy metrics, studying the differences between the predicted and actual user ratings. However, recent research has found accuracy to underperform when systems go live, mainly due to accuracy’s inability to validate recommendation lists as a single entity, and shifted to evaluating recommender systems using "beyond-accuracy" metrics, like novelty and diversity. In this dissertation, we summarize and organize the leading research regarding the definitions and objectives of the beyond-accuracy metrics. Such metrics include coverage, diversity, novelty, serendipity, unexpectedness, utility, and fairness. The behaviors and relationships of these metrics are analyzed using four different models, two concerning the items characteristics (item-based) and two regarding the user behaviors (user-based). Furthermore, a new metric is proposed that allows the comparison of different models considering their overall beyond-accuracy performance. Using this metric, a reraking approach is designed to improve the performance of a system, aiming to achieve better recommendations. The impact of the reranking technique on each metric and algorithm is studied, and the accuracy and non-accuracy performance of each system is compared. We realized that, although the reranking technique can increase most beyond-accuracy metrics, the accuracy of that system starts to worsen due to the negative correlation between these two dimensions. We also found that item-based models tend to achieve much lower values of coverage and diversity than userbased models

People know how diverse their music recommendations should be; why don’t we?

While many researchers have proposed various ways of quantifying recommendation list diversity, these approaches have had little input from users on their own perceptions and preferences in seeking diversity. Through a set of user studies we provide a better understanding of how users view the concept of diversity in music recommendations, and how intra-list diversity can be adapted to better represent their diversity preference. Our results show that users have a clear idea of what music recommendation diversity means to them, accuracy metrics do not model overall list satisfaction, and filtering recommendations on genre before list diversification can positively impact list satisfaction. More importantly, our results highlight the need to base music recommendation metrics on insights from real peopl