Enhancing User Personalization in Conversational Recommenders

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.Comment: To Appear On TheWebConf (WWW) 202

Meta Policy Learning for Cold-Start Conversational Recommendation

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge. In this paper, we study CRS policy learning for cold-start users via meta-reinforcement learning. We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations. To facilitate fast policy adaptation, we design three synergetic components. Firstly, we design a meta-exploration policy dedicated to identifying user preferences via a few exploratory conversations, which accelerates personalized policy adaptation from the meta policy. Secondly, we adapt the item recommendation module for each user to maximize the recommendation quality based on the collected conversation states during conversations. Thirdly, we propose a Transformer-based state encoder as the backbone to connect the previous two components. It provides comprehensive state representations by modeling complicated relations between positive and negative feedback during the conversation. Extensive experiments on three datasets demonstrate the advantage of our solution in serving new users, compared with a rich set of state-of-the-art CRS solutions.Comment: 10 pages, WSDM202

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

A personality-aware group recommendation system based on pairwise preferences

Human personality plays a crucial role in decision-making and it has paramount importance when individuals negotiate with each other to reach a common group decision. Such situations are conceivable, for instance, when a group of individuals want to watch a movie together. It is well known that people influence each other’s decisions, the more assertive a person is, the more influence they will have on the final decision. In order to obtain a more realistic group recommendation system (GRS), we need to accommodate the assertiveness of the different group members’ personalities. Although pairwise preferences are long-established in group decision-making (GDM), they have received very little attention in the recommendation systems community. Driven by the advantages of pairwise preferences on ratings in the recommendation systems domain, we have further pursued this approach in this paper, however we have done so for GRS. We have devised a three-stage approach to GRS in which we 1) resort to three binary matrix factorization methods, 2) develop an influence graph that includes assertiveness and cooperativeness as personality traits, and 3) apply an opinion dynamics model in order to reach consensus. We have shown that the final opinion is related to the stationary distribution of a Markov chain associated with the influence graph. Our experimental results demonstrate that our approach results in high precision and fairness.Spanish Government PID2019-10380RBI00/AEI/10. 13039/501100011033Andalusian Government P20_0067

Joint Session-Item Encoding for Session-Based Recommendation: A Metric- Learning Approach with Temporal Smoothing

In recommendation systems, a system is in charge of providing relevant recommendations towards users with either a clear target in mind or a mere vague mental representation. Session-based recommendation targets a specific scenario in recommendation systems, where users are anonymous. Thus the recommendation system must work under more challenging conditions, having only the current session to extract any user preferences to provide recommendations. This setting requires a model capable of understanding and relating different inter- actions across different sessions involving different items. This dissertation reflects such relationships on a commonly learned space for sessions and items. Such space is built using metric-learning, which can capture such relationships and build such space, where the distances between the elements (session and item embeddings) reflect how they relate to each other. We then use this learned space as the intermediary to provide relevant rec- ommendations. This work continues and extends on top of other relevant work showing the potential of metric-learning addressed to the session-based recommendation field. This dissertation proposes three significant contributions: (i) propose a novel joint session-item encoding model with temporal smoothing, with fewer parameters and the inclusion of temporal characteristics in learning (temporal proximity and temporal re- cency); (ii) enhanced recommendation performance surpassing other state-of-the-art metric-learning models for session-based recommendation; (iii) a thorough critical analy- sis, addressing and raising awareness to common problems in the field of session-based recommendation, discussing the reasons behind them and their impact on model perfor- mance.Em sistemas de recomendação, um sistema fica encarregue de fornecer recomendações relevantes aos seus utilizadores que podem ter, ou uma ideia concreta daquilo que pre- tendem ou apenas uma vaga representação mental. Recomendação com base na sessão dirige-se principalmente a um cenário específico de sistemas de recomendação, onde os utilizadores são anónimos. Ou seja, estes sistemas têm de ser capazes de funcionar em condições mais desfavoráveis, tendo apenas a sessão atual disponível como input do utilizador para efetuar recomendações. Este contexto requer um modelo capaz de perceber e relacionar diferentes interações ao longo de várias outras sessões envolvendo diferentes itens. Esta dissertação reflete tais interações por via de um espaço comum, que é aprendido, para representar sessões e itens. Este espaço é construído usando metric-learning, técnica que consegue capturar tais relações e construir o espaço em questão, no qual a distância entre os vários elementos (embeddings de sessões e itens) reflete como estes se relacionam entre si. Usamos este espaço, que foi aprendido, como intermediário no fornecimento de recomendações rele- vantes. Este trabalho continua e extende para além de outros trabalhos relevantes na área que mostraram o potencial de aplicar metric-learning para o domínio de recomendação com base na sessão. Esta dissertação propõe as seguintes três principais e significativas contribuições: (i) propõe um novo modelo de codificação sessão-item conjunto com suavização temporal, com menos parâmetros e com a inclusão de características temporais no processo de aprendizagem (proximidade temporal e recência); (ii) um desempenho de recomenda- ção melhorado que ultrapassa outros métodos do estado-da-arte que utilizam técnicas de metric-learning para sistemas de recomendação com base na sessão; (iii) uma análise cuidada, que foca e tenta destacar alguns erros comuns neste campo de sistemas de re- comendação com base na sessão, discutindo as razões por detrás de tais erros e o seu impacto no desempenho dos modelos

Active learning in recommender systems: an unbiased and beyond-accuracy perspective

The items that a Recommender System (RS) suggests to its users are typically ones that it thinks the user will like and want to consume. An RS that is good at its job is of interest not only to its customers but also to service providers, so they can secure long-term customers and increase revenue. Thus, there is a challenge in building better recommender systems. One way to build a better RS is to improve the quality of the data on which the RS model is trained. An RS can use Active Learning (AL) to proactively acquire such data, with the goal of improving its model. The idea of AL for RS is to explicitly query the users, asking them to rate items which have not been rated yet. The items that a user will be asked to rate are known as the query items. Query items are different from recommendations. For example, the former may be items that the AL strategy predicts the user has already consumed, whereas the latter are ones that the RS predicts the user will like. In AL, query items are selected `intelligently' by an Active Learning strategy. Different AL strategies take different approaches to identify the query items. As with the evaluation of RSs, preliminary evaluation of AL strategies must be done offline. An offline evaluation can help to narrow the number of promising strategies that need to be evaluated in subsequent costly user trials and online experiments. Where the literature describes the offline evaluation of AL, the evaluation is typically quite narrow and incomplete: mostly, the focus is cold-start users; the impact of newly-acquired ratings on recommendation quality is usually measured only for those users who supplied those ratings; and impact is measured in terms of prediction accuracy or recommendation relevance. Furthermore, the traditional AL evaluation does not take into account the bias problem. As brought to light by recent RS literature, this is a problem that affects the offline evaluation of RS; it arises when a biased dataset is used to perform the evaluation. We argue that it is a problem that affects offline evaluation of AL strategies too. The main focus of this dissertation is on the design and evaluation of AL strategies for RSs. We first design novel methods (designated WTD and WTD_H) that `intervene' on a biased dataset to generate a new dataset with unbiased-like properties. Compared to the most similar approach proposed in the literature, we give empirical evidence, using two publicly-available datasets, that WTD and WTD_H are more effective at debiasing the evaluation of different recommender system models. We then propose a new framework for offline evaluation of AL for RS, which we believe facilitates a more authentic picture of the performances of the AL strategies under evaluation. In particular, our framework uses WTD or WTD_H to mitigate the bias, but it also assesses the impact of AL in a more comprehensive way than the traditional evaluation used in the literature. Our framework is more comprehensive in at least two ways. First, it segments users in more ways than is conventional and analyses the impact of AL on the different segments. Second, in the same way that RS evaluation has changed from a narrow focus on prediction accuracy and recommendation relevance to a wider consideration of so-called `beyond-accuracy' criteria (such as diversity, serendipity and novelty), our framework extends the evaluation of AL strategies to also cover `beyond-accuracy' criteria. Experimental results on two datasets show the effectiveness of our new framework. Finally, we propose some new AL strategies of our own. In particular, our new AL strategies, instead of focusing exclusively on prediction accuracy and recommendation relevance, are designed to also enhance `beyond-accuracy' criteria. We evaluate the new strategies using our more comprehensive evaluation framework