Comparative preferences induction methods for conversational recommenders

In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user’s preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study

Evaluating Conversational Recommender Systems: A Landscape of Research

Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique

Effective graph representation learning for ranking-based recommendation

Ranking-based recommender systems are designed to generate a personalised ranking list of items for a given user to address the information overload problem. An effective and efficient ranking-based recommender system can benefit users by providing them with items of interest as well as service providers by increasing their exposure and profits. Since more and more users and providers of items have been increasingly interacting with online platforms, the underlying recommendation algorithms are facing more challenges. For example, traditional collaborative filtering-based recommender systems cannot generate effective recommendations to cold-start users due to the lack of sufficient interactions. In addition, although recommender systems can leverage deep learning-based techniques to enhance their effectiveness, they are not robust enough against variances in the models’ initialisations, which can degrade the users’ satisfaction. Furthermore, when incorporating these complex deep models, the training phases of recommender systems become less efficient, which might slower the online platforms from quickly capturing the users’ interests. Graph representation learning includes techniques that can leverage graph-structured data and generate latent representations for the nodes, graphs/sub-graphs and edges between nodes. Since the user-item interaction matrix is in fact a bipartite graph, we can use these graph-based techniques to leverage the interaction matrix and generate more effective node representations for the users and items. Therefore, this thesis aims to enhance the ranking-based recommendations by proposing novel recommender systems based on graph representation learning. In particular, this thesis uses heterogeneous graph representation learning, graph pre-training and graph contrastive learning to improve the effectiveness of ranking-based recommendations while alleviating the aforementioned cold-start problem as well as the low-robustness and low training-efficiency issues. To enhance the effectiveness of ranking-based recommendations and alleviate the cold-start problem, we propose to use the heterogeneous graph representation learning technique to encode the typical side information of the users and items, which are usually defined as the attributes of users and the descriptions of items. For example, a user-item interaction matrix, social relations are one of the most naturally available relations that can be used to enrich such an interaction matrix. Therefore, we choose the social relations among different types of side information to build the heterogeneous graph. We propose a novel recommender system, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data using the heterogeneous graph representation learning technique. Next, in the subsequent fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to factorise these pre-trained embeddings for further training. Our extensive experiments on three public datasets show that SGP can alleviate the cold-start problem while also ensuring effective recommendations for regular users. To alleviate the low-robustness issue and enhance the recommendation effectiveness, we propose to leverage multiple types of side information using the graph pre-training technique. In particular, we aim to generalise the pre-training technique used by SGP for multiple types of side information associated with both users and items. Specifically, we propose two novel pre-training schemes, namely Single-P and Multi-P, to leverage side information such as the ages and occupations of users and the textual reviews and categories of items. Instead of jointly training with two objectives, our pre-training schemes first pre-train a representation model under the users and items’ multi/single relational graphs constructed by their side information and then fine-tune their embeddings under an existing general representation-based recommendation model. Extensive experiments on three public datasets show that the graph pre-training technique can effectively enhance the effectiveness of ranking-based recommender systems and alleviates the cold-start problem. In addition, our pre-training schemes can provide more ef-fective initialisations for both the users and items; hence the robustness of fine-tuning models namely MF, NCF, NGCF and LightGCN, can be improved. Finally, to enhance the training efficiency of graph-based recommenders while ensuring their effectiveness, we propose to use the graph contrastive learning technique to improve the traditional random negative sampling approach. In particular, we propose a dynamic negative sampling (DNS) approach that leverages the graph contrastive learning technique to replace the randomly sampled negative items with more informative negative items. Our experiments show that DNS can improve the recommendation effectiveness of four competitive recommenders. Next, we further propose a novel graph-based model, i.e. MLP-CGRec, that leverages a multiple sampling approach to enhance the training efficiency of the graph-based recommender system. In particular, MLP-CGRec uses DNS to sample contrastive negative items and an efficient graph-based sampling method to select pseudo-positive samples. Experimental results on three public datasets show that MLP-CGRec can maintain competitive effectiveness and achieve the best efficiency compared with state-of-the-art recommender systems

User Satisfaction with Personalised Internet Applications

The study focuses on user satisfaction with websites and personalised internet applications in particular. The abundance of information on the web is increasing more and more. Therefore, the significance of websites targeting the users’ preferences, like personalised Internet applications, is rising. The aim of this study was to find out which factors determine user satisfaction with personalised internet applications. Factors like the usefulness of the information or trust towards how personal information is handled were considered. A large-scale user survey evaluating three internet applications (from the travel, e-learning and real estate domains) was conducted. Expert opinions were collected to complement the results and provide insights from users’ and experts’ points of views

User Satisfaction with Personalised Internet Applications

The study focuses on user satisfaction with websites and personalised internet applications in particular. The abundance of information on the web is increasing more and more. Therefore, the significance of websites targeting the users’ preferences, like personalised Internet applications, is rising. The aim of this study was to find out which factors determine user satisfaction with personalised internet applications. Factors like the usefulness of the information or trust towards how personal information is handled were considered. A large-scale user survey evaluating three internet applications (from the travel, e-learning and real estate domains) was conducted. Expert opinions were collected to complement the results and provide insights from users’ and experts’ points of views

Predictive statistical user models under the collaborative approach

Mención Internacional en el título de doctorUser models and recommender systems due to their similarity can be considered the same thing except from the use that we make of them. Both have their root in multiple disciplines such as information retrieval or machine learning among others. The impact has grown rapidly with the importance of data on systems and applications. Most of the big companies employ one of the other for different reasons such as: gathering more customers, boost sales or increase revenue. Thus very well-known companies like Amazon, EBay or Google use models to improve their businesses. In fact, as data becomes more and more important for companies, universities and people, user models are crucial to make decisions over large amounts of data. Although user models can provide accurate predictions on large populations their use and application is not restricted to predictions but can be extended to selection of dialogue strategies or detection of communities within complex domains. After a deep review of the existing literature, it was found that there is a lack of statistical user models based on experience plus the existing models in the area are content-based models that suffer from major problems as scalability, cold-start or new user problem. Furthermore, researchers in the area of user modelling usually develop their own models and then perform ad-hoc evaluations that are not replicable and therefore not comparable. The lack of a complete framework for evaluation makes very difficult to compare results across models and domains. There are two main approaches to build a user model or recommender system: the content based approach, where predictions are based on the same user past behaviours; and the collaborative approach where predictions rely on like-minded people. Both approaches have advantages but also downsides that have to be considered before building a model. The main goal of this thesis is to develop a hybrid user model that takes the strengths of both approaches and mitigates the downsides by combining both methods. The proposed hybrid model is based on an R-Tree structure. The selection of this structure to support the models is backed from the fact that the rectangle tree is specifically designed to effectively store and manipulate multidimensional data. This data structure introduced by Guttman in 1984 is a height balanced tree that only requires visiting a few nodes to perform a tree search. As a result, it can manage large populations of data efficiently as only a few nodes are visited during the inference. R-Tree has two different typologies of nodes: the leaf-node and the non-leaf node. Leaf nodes contain the whole universe of users while non leaf nodes are somehow redundant and contain summaries of child nodes. Along this thesis two statistical user models based on experience have been proposed. The first one is a knowledge base user mode (KLUM), is a classical approach that summarizes and remove data in order to keep performance level within reasonable margins. The second one, an R-Tree user model (RTUM), is an innovative model based on an R-Tree structure. This new model not only solves the problem of removing data but also the scalability problem which turns out to be one of the major problems in the area of user modelling. Both models have been developed and tested with equivalent formulations to make comparisons relevant. Both models are prepared to create their own knowledge base from scratch but also they can be fed with expert knowledge. Thus alleviating another major problem in the area of user modelling as it is the start-up problem. Regarding the proposal of this thesis, two statistical user models are proposed (KLUM and RTUM). In addition, a refinement of RTUM user model is proposed, while RTUM performs node partitions based on the centroids of the users in that node, the new refinement implements a new partition based on privileged features. Hence, the new approach takes advantage of most discriminatory features of the domain to perform the partition. This new approach not only provides accurate inferences, but also an excellent clustering that can be useful in many different scenarios. For instance, this clustering can be employed in the area of social networks to detect communities within the social network. This is a tough task that has been one of the goals of many researchers during the last few years. This thesis also provides a complete evaluation of the models with a great diversity of parameterizations and domains. The models are tested in four different domains and as a result of the evaluation, it is proved that RTUM user model provides a massive gain against classical user models as KLUM. During the evaluation, RTUM reached success rates of 85% while the analogous KLUM could only reach a 65% thus leaving a 20% gain for the proposed model. The evaluation provided not only compares models and success rates, but also provides a broad analysis of how every parameter of the models impact the performance plus a complete study of the databases sizes and inference times for the models. The main conclusion to the evaluation is that after a complete evaluation with a wide diversity of parameters and domains RTUM outperforms KLUM on every scenario tested. As previously mentioned, after the literature review it was also found a lack of evaluation frameworks for user modelling. This thesis also provides a complete evaluation framework for user modelling. This fills a gap in the literature as well as makes the evaluation replicable and therefore comparable. Along years researchers and developers had found difficulties to compare evaluations and measure the quality of their models in different domains due to the lack of an evaluation standard. The evaluation framework presented in this thesis covers data samples including training set and test set plus different sets of experiments alongside with a statistical analysis of the domain, confidence intervals and confidence levels to guarantee that each experiment is statistically significant. The evaluation framework can be downloaded and then used to complete evaluations and cross-validate results across different models.This thesis would not have been possible without the financial support of the following research projects Cadooh (TSI-020302-2011-21), Thuban (TIN2008-02711) that funded part of this research.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Antonio de Amescua Seco.- Secretario: Ruth Cobos Pérez.- Vocal: Dominikus Heckman

Advanced techniques for personalized, interactive question answering

Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with naturallangilage to enable the computer to understand what its user asks. The discipline that studies the conD:ection between natural language and the represen~ tation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language, aims at finding one or more concise answers in the form of sentences or phrases. Question Answering can be interpreted as a sub-discipline of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text. Although it is widely accepted that Question Answering represents a step beyond standard infomiation retrieval, allowing a more sophisticated and satisfactory response to the user's information needs, it still shares a series of unsolved issues with the latter. First, in most state-of-the-art Question Answering systems, the results are created independently of the questioner's characteristics, goals and needs. This is a serious limitation in several cases: for instance, a primary school child and a History student may need different answers to the questlon: When did, the Middle Ages begin? Moreover, users often issue queries not as standalone but in the context of a wider information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces would encourage and accommodate the submission of multiple related questions and handle the user's requests for clarification, interactive Question Answering is still at its early stages: Furthermore, an i~sue which still remains open in current Question Answering is that of efficiently answering complex questions, such as those invoking definitions and descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess the correctness of answers to such complex questions. .. These are the central research problems addressed by this thesis, and are solved as follows. An in-depth study on complex Question Answering led to the development of classifiers for complex answers. These exploit a variety of lexical, syntactic and shallow semantic features to perform textual classification using tree-~ernel functions for Support Vector Machines. The issue of personalization is solved by the integration of a User Modelling corn': ponent within the the Question Answering model. The User Model is able to filter and fe-rank results based on the user's reading level and interests. The issue ofinteractivity is approached by the development of a dialogue model and a dialogue manager suitable for open-domain interactive Question Answering. The utility of such model is corroborated by the integration of an interactive interface to allow reference resolution and follow-up conversation into the core Question Answerin,g system and by its evaluation. Finally, the models of personalized and interactive Question Answering are integrated in a comprehensive framework forming a unified model for future Question Answering research