Interaction design guidelines on critiquing-based recommender systems

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage-one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid-system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system

Conversational Recommender System: Berbasis pada Kebutuhan Fungsional Produk

Menyatakan kebutuhan berdasarkan fitur teknis produk sering menyulitkan banyak calon pembeli, khususnya untuk produk multi fungsi dan mempunyai banyak fitur, seperti mobil, notebook, smartphone, server, kamera, dan sebagainya, dsb-dan sebagainya. Hal ini dikarenakan tidak semua orang familiar terhadap fitur teknis dari produk-produk tersebut. Menanyakan kebutuhan pengguna aspek kegunaan (kebutuhan fungsional) dari produk yang akan dibeli, adalah cara yang lebih natural dalam menggali kebutuhan pengguna. Oleh karena itu, buku ini menyajikan bagaimana membangun sebuah conversational recommender system (CRS) yang memperhatikan aspek kebutuhan fungsional produk. Ontologi dipilih sebagai pengetahuan dari sistem, karena nature dari struktur ontologi, memungkinkan untuk membuat pemetaan yang lebih fleksibel antara kebutuhan fungsional produk, spesifikasi, dan produk. Selain itu, dalam ontologi, memungkinkan untuk penyusunan masingmasing konsep (entitas) secara hirarkis, dan struktur seperti ini sangat menguntungkan, terutama untuk mendukung pengembangan model pembangkitan pertanyaan. Struktur ontologi ini mempunyai 3 kelas utama, yaitu FuncReq (merepresentassikan kebutuhan fungsional), Specification (merepresentasikan gradasi kualitas fitur teknis) dan Product (merepresentasikan klasifikasi produk). Ontologi merupakan basis pengetahuan dari sistem. Mekanisme interaksi dilakukan melalui dialog tanya jawab, rekomendasi produk dan penjelasan mengapa suatu produk direkomendasikan, seperti layaknya interaksi antara calon pembeli dengan professional sales support. Model komputasional untuk membangkitkan interaksi dikembangkan dengan memanfaatkan eksplorasi relasi semantik dalam ontologi. Dengan model dan struktur ontologi ini, diharapkan pengembangan CRS yang disajikan dalam buku ini, dapat juga diterapkan untuk berbagai domain yang berbeda, khususnya untuk domain produk yang bersifat multi fungsi dan mempunyai banyak fitur (notebook, server, PC, mobil, kamera, smartphone, dan sebagainya, dsbdan sebagainya). iv Conversational Recommender System Berbasis Pada Kebutuhan Fungsional Produk Evaluasi terhadap CRS yang dibangun meliputi evaluasi dari sisi efisiensi maupun efektifitas. Hasil evaluasi menunjukkan bahwa model interaksi dalam CRS berbasis kebutuhan fungsional mampu melakukan mekanisme query requirement dengan efisien, berdasarkan pengurangan jumlah sisa record secara signifikan dalam 4 interaksi. Dalam 4 interaksi, jumlah produk yang direkomendasikan kurang dari 20 dari 288 produk yang ada (< 0.6.9%). Dari sisi efektifitas, dilakukan user study yang melibatkan pengguna yang familiar (expert user) maupun tidak familiar (novice user) dengan fitur teknis produk. Hasil pengujian menunjukkan, CRS berbasis kebutuhan fungsional cukup efektif dalam memandu pengguna. Hal ini ditunjukkan dengan, baik expert maupun novice user lebih menyukai model interaksi CRS berbasis kebutuhan fungsional daripada model interaksi pada aplikasi pencarian produk berbasis pada fitur teknis produk (expert user: 86.67%, novice user: 90%). User study selanjutnya menunjukkan, interaksi dalam CRS berbasis kebutuhan fungsional mampu meningkatkan persepsi positif pengguna, dibandingkan dengan interaksi yang berbasis pada fitur teknis produk, dilihat dari perceived ease of use, perceived enjoyment, trust dan perceived usefulness. Selain itu, model interaksi juga efektif dalam mempengaruhi pengguna untuk tertarik mengadopsi sistem, namun terdapat perbedaan dalam faktor-faktor yang mempengaruhi hal tersebut. Untuk expert user, perceived enjoyment merupakan faktor yang mempengaruhi secara langsung untuk adopsi sistem, sedangkan perceived usefulness merupakan faktor yang secara langsung mempengaruhi adopsi sistem, bagi novice use

Recommendation in Dialogue Systems

Dialogue system has been an active research field for decades and is developing fast in recent years, due to the recent breakthrough of the deep learning techniques. How to make recommendations in dialogue systems is attracting increasing attention because such systems could meet various user information needs and have much commercial potential.Current dialogue system researches typically focus on building systems for social conversation, question answering, and performing specific tasks. However, making recommendations to users, as important information need, has not been intensively researched. Meanwhile, traditional recommender systems are usually developed for non-conversation scenarios. In this dissertation, we explore how to integrate these two systems into one framework that specifically aims at making recommendations in dialogues. Such a system helps users find items by chatting with users to understand their preferences and recommending accordingly.First, we build conversational recommendation datasets, because existing dialogue datasets do not have user-item preference information or the dialogue utterances discussing facets of items, and current recommendation datasets do not have dialogue scripts associated with each user-item pair. We build the datasets by requesting crowdsourcing workers to compose dialogue utterances based on schemas and then use the delexicalization approach to simulate dialogues with the collected utterances. The datasets are used to train the natural language understanding component and provide recommendation information for our system.Based on collected datasets, we propose a reinforcement learning based conversational recommendation framework. Such a framework has three components, a belief tracker, a dialogue manager, and a recommender. The dialogue agent learns to first chat with a user to understand her preferences, and when it feels confident enough, it recommends a list of items to the user. We conduct both offline and online experiments to demonstrate the effectiveness of the framework.We further extend this framework with a personalized probabilistic recommender module. This recommender learns to predict the probability of a user likes an item given the dialogue utterance information and the personalized user preference information. By leveraging this hybrid information, the recommendation and dialogue performances are further improved. We evaluate the dialogue agent's strength in various simulated environments as well as in online user studies and demonstrate the advantages of this approach

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

Social contextuality and conversational recommender systems

As people continue to become more involved in both creating and consuming information, new interactive methods of retrieval are being developed. In this thesis we examine conversational approaches to recommendation, that is, the act of suggesting items to users based on the system’s understanding of them. Conversational recommendation is a recent contribution to the task of information discovery. We propose a novel approach to conversation around recommendation, examining how it is improved to work with collaborative filtering, a common recommendation algorithm. In developing new ways to recommend information to people we also examine their methods of information seeking, exploring the role of conversational recommendation, using both interview and sensed brain signals. We also look at the implications of the wealth of social and sensed information now available and how it improves the task of accurate recommendation. By allowing systems to better understand the connections between users and how their social impact can be tracked we show improved recommendation accuracy. We look at the social information around recommendations, proposing a directed influence approach between socially connected individuals, for the purpose of weighting recommendations with the wisdom of influencers. We then look at the semantic relationships that might seem to indicate wisdom (i.e. authors on a book-ranking site) to see if the ``wisdom of the few'' can be traced back to those conventionally considered wise in the area. Finally we look at ``contextuality'' (the ability of sets of contextual sensors to accurately recommend items across groups of people) in recommendation, showing that different users have very different uses for context within recommendation. This thesis shows that conversational recommendation can be generalised to work well with collaborative filtering, that social influence contributes to recommendation accuracy, and that contextual factors should not be treated the same for each user

Improving user confidence in decision support systems for electronic catalogs

Decision support systems for electronic catalogs assist users in making the right decision from a set of possible choices. Common examples of decision making include shopping, deciding where to go for holidays, or deciding your vote in an election. Current research in the field is mainly focused on improving such systems in terms of decision accuracy, i.e. the ratio of correct decisions out of the total number of decisions taken. However, it has been widely recognized recently that another important dimension to consider is how to improve decision confidence, i.e. the certainty of the decision maker that she has made the best decision. We first review multi-attribute decision theory –the underlying framework for electronic catalogs– and present the state-of-the-art research in e-catalogs. We then describe objective and subjective measures to evaluate such systems, and propose a system baseline for achieving more accurate and meaningful comparative evaluations. We propose a framework to study the building of decision confidence within the query-feedback search interaction model, and use it to compare different types of system feedback proposed in the literature. We argue that different types of system feedback based on constraints (e.g. conflict and corrective feedback), even if not novel as such, can be combined in order to improve decision confidence. This claim is further validated by simulations and experimental evaluation comparing constraint-based feedback to ranked list feedback