News topic classification as a first step towards diverse news recommendation

When developing an algorithm that uses news diversity as a key driver for personalized news recommendation it is crucial to focus on means to cluster news articles in a fine-grained manner, ideally by leveraging the content of the text. In this paper we investigate semantic classification of news articles in an unfiltered news stream. We first present an analysis of the EventDNA corpus: a collection of Dutch-language news articles annotated with event data according to a predefined typology. We found that the types assigned as features of events do not allow for such a semantic classification and investigate the IPTC News Media Topics standard as an alternative. By mapping event types with manually-assigned IPTC topics, we observe that a more diversified picture emerges, which leads us to conclude that the IPTC classification is a useful proxy. Based on a historical data sample of Dutch news articles covering the year 2018, we then perform a series of machine learning experiments in order to automatically predict the top two levels of the IPTC taxonomy. Various multi-label classification models are built with BERTje using a bottom-up and top-down approach. The results reveal that the top-down approach yields the best results, with an overall macro F-1 score of 86.4% and a Jaccard accuracy of 89.2% for the level-one topics and one of 83.7% and 87.5% for the level-two predictions

UTSP: User-Based Two-Step Recommendation with Popularity Normalization towards Diversity and Novelty

© 2013 IEEE. Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top-N recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics

Performative Recommendation: Diversifying Content via Strategic Incentives

The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking improve diversity by presenting more diverse items. Here we argue that to promote inherent and prolonged diversity, the system must encourage its creation. Towards this, we harness the performative nature of recommendation, and show how learning can incentivize strategic content creators to create diverse content. Our approach relies on a novel form of regularization that anticipates strategic changes to content, and penalizes for content homogeneity. We provide analytic and empirical results that demonstrate when and how diversity can be incentivized, and experimentally demonstrate the utility of our approach on synthetic and semi-synthetic data

Towards Diversity in Recommendations Using Social Networks

While there has been a lot of research towards improving the accuracy of recommender systems, the resulting systems have tended to become increasingly narrow in suggestion variety. An emerging trend in recommendation systems is to actively seek out diversity in recommendations, where the aim is to provide unexpected, varied, and serendipitous recommendations to the user. Our main contribution in this paper is a new approach to diversity in recommendations called "Social Diversity," a technique that uses social network information to diversify recommendation results. Social Diversity utilizes social networks in recommender systems to leverage the diverse underlying preferences of different user communities to introduce diversity into recommendations. This form of diversification ensures that users in different social networks (who may not collaborate in real life, since they are in a different network) share information, helping to prevent siloization of knowledge and recommendations. We describe our approach and show its feasibility in providing diverse recommendations for the MovieLens dataset

Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations

Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individual-user level, it tailors them on a user's propensity towards the different objectives. Hence, the capability to understand users' fine-grained needs towards each goal is crucial. In this paper, we present the results of a user study in which we monitored the way users interacted with recommended items, as well as their self-proclaimed propensities towards relevance, novelty, and diversity objectives. The study was divided into several sessions, where users evaluated recommendation lists originating from a relevance-only single-objective baseline as well as MORS. We show that, despite MORS-based recommendations attracting fewer selections, their presence in the early sessions are crucial for users' satisfaction in the later stages. Surprisingly, the self-proclaimed willingness of users to interact with novel and diverse items is not always reflected in the recommendations they accept. Post-study questionnaires provide insights on how to deal with this matter, suggesting that MORS-based results should be accompanied by elements that allow users to understand the recommendations, so as to facilitate the choice of whether a recommendation should be accepted or not. Detailed study results are available at https://bit.ly/looks-can-be-deceiving-repo

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations. However, existing approaches for LLM4Rec often assess performance using restricted sets of candidates, which may not accurately reflect the models' overall ranking capabilities. In this paper, our objective is to investigate the comprehensive ranking capacity of LLMs and propose a two-step grounding framework known as BIGRec (Bi-step Grounding Paradigm for Recommendation). It initially grounds LLMs to the recommendation space by fine-tuning them to generate meaningful tokens for items and subsequently identifies appropriate actual items that correspond to the generated tokens. By conducting extensive experiments on two datasets, we substantiate the superior performance, capacity for handling few-shot scenarios, and versatility across multiple domains exhibited by BIGRec. Furthermore, we observe that the marginal benefits derived from increasing the quantity of training samples are modest for BIGRec, implying that LLMs possess the limited capability to assimilate statistical information, such as popularity and collaborative filtering, due to their robust semantic priors. These findings also underline the efficacy of integrating diverse statistical information into the LLM4Rec framework, thereby pointing towards a potential avenue for future research. Our code and data are available at https://github.com/SAI990323/Grounding4Rec.Comment: 17 page

User centred evaluation of a recommendation based image browsing system

In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful

Recomendation systems and crowdsourcing: a good wedding for enabling innovation? Results from technology affordances and costraints theory

Recommendation Systems have come a long way since their first appearance in the e-commerce platforms.Since then, evolved Recommendation Systems have been successfully integrated in social networks. Now its time to test their usability and replicate their success in exciting new areas of web -enabled phenomena. One of these is crowdsourcing. Research in the IS field is investigating the need, benefits and challenges of linking the two phenomena. At the moment, empirical works have only highlighted the need to implement these techniques for tasks assignment in crowdsourcing distributed work platforms and the derived benefits for contributors and firms. We review the variety of the tasks that can be crowdsourced through these platforms and theoretically evaluate the efficiency of using RS to recommend a task in creative crowdsourcing platforms. Adopting a Technology Affordances and Constraints Theory, an emerging perspective in the Information Systems (IS) literature to understand technology use and consequences, we anticipate the tensions that this implementation can generate

Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

Adapting Recommendation Diversity to Openness to Experience : A Study of Human Behaviour

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