Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202

On Interpretation and Measurement of Soft Attributes for Recommendation

We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.publishedVersio

Controllable Recommenders using Deep Generative Models and Disentanglement

In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. While deep models have shown improved performance for collaborative filtering, they are generally not amenable to fine grained control by a user, leading to the development of methods like deep language critiquing. We propose an alternate view, where instead of keyphrase based critiques, a user is provided 'knobs' in a disentangled latent space, with each knob corresponding to an item aspect. Disentanglement here refers to a latent space where generative factors (here, a preference towards an item category like genre) are captured independently in their respective dimensions, thereby enabling predictable manipulations, otherwise not possible in an entangled space. We propose using a (semi-)supervised disentanglement objective for this purpose, as well as multiple metrics to evaluate the controllability and the degree of personalization of controlled recommendations. We show that by updating the disentangled latent space based on user feedback, and by exploiting the generative nature of the recommender, controlled and personalized recommendations can be produced. Through experiments on two widely used collaborative filtering datasets, we demonstrate that a controllable recommender can be trained with a slight reduction in recommender performance, provided enough supervision is provided. The recommendations produced by these models appear to both conform to a user's current preference and remain personalized.Comment: 10 pages, 1 figur

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Enhancing explainability and scrutability of recommender systems

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modified accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ profiles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for finding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-specific item representations. We evaluate all proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen für maschinelle Empfehlungen erfordert Modelle und Methoden für erklärbare, nachvollziehbare und vertrauenswürdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die Lücke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schließen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein Übermaß an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu filtern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen für deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre früheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. Außerdem können Erklärungen für den Fall, dass unerwünschte Inhalte empfohlen werden, wertvolle Informationen darüber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. • Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen für die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese Erklärungen zeigen Beziehungen zwischen Benutzerprofilen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. • Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen für PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind für Benutzer verständlich, da sie Teilmengen früherer Nutzerinteraktionen darstellen, die für die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu finden. • Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspezifischer Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten

Revisiting the Tag Relevance Prediction Problem

Traditionally, recommender systems provide a list of suggestions to a user based on past interactions with items of this user. These recommendations are usually based on user preferences for items and generated with a delay. Critiquing recommender systems allow users to provide immediate feedback to recommendations with tags and receive a new set of recommendations in response. However, these systems often require rich item descriptions that contain relevance scores indicating the strength, with which a tag applies to an item. For example, this relevance score could indicate how violent the movie "The Godfather" is on a scale from 0 to 1. Retrieving these data is a very demanding process, as it requires users to explicitly indicate the degree to which a tag applies to an item. This process can be improved with machine learning methods that predict tag relevance. In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs. In particular, we define the tag relevance prediction problem, explore the inconsistency of relevance scores provided by users as a challenge of this problem and present a method, which outperforms the state-of-the-art method for predicting tag relevance. We found a moderate inconsistency of user relevance scores. We also found that users tend to disagree more on subjective tags, such as "good acting", "bad plot" or "quotable" than on objective tags, such as "animation", "cars" or "wedding", but the disagreement of users regarding objective tags is also moderate.Peer reviewe