A major challenge in recommender systems is handling new users, whom are also called @math users. In this paper, we propose a novel approach for learning an optimal series of questions with which to interview cold-start users for movie recommender systems. We propose learning interview questions using Deep Q Networks to create user profiles to make better recommendations to cold-start users. While our proposed system is trained using a movie recommender system, our Deep Q Network model should generalize across various types of recommender systems.
One successful method for addressing cold-start is a preliminary interview process to learn user's interests for good recommendation. @cite_8 propose building a decision tree for the initial interview process by bootstrapping. They construct a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to his her prior responses.
@cite_8
2013221919
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree --- in effect restricting the latent profiles to be a function of possible answers to the interview questions --- which allows the profiles to be gradually refined through the interview process based on user responses. We develop an iterative optimization algorithm that alternates between decision tree construction and latent profiles extraction as well as a regularization scheme that takes into account of the tree structure. Experimental results on three benchmark recommendation data sets demonstrate that the proposed fMF algorithm significantly outperforms existing methods for cold-start recommendation. For cold-start recommendation, it is important to rapidly profile new users and generate a good initial set of recommendations through an interview process --- users should be queried adaptively in a sequential fashion, and multiple items should be offered for opinion solicitation at each trial. In this work, we propose a novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split. The splits, represented as sparse weight vectors, are learned through an L_1-constrained optimization framework. The users are directed to child nodes according to the inner product of their responses and the corresponding weight vector. More importantly, to account for the variety of responses coming to a node, a linear regressor is learned within each node using all the previously obtained answers as input to predict item ratings. A user study, preliminary but first in its kind in cold-start recommendation, is conducted to explore the efficient number and format of questions being asked in a recommendation survey to minimize user cognitive efforts. Quantitative experimental validations also show that the proposed algorithm outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts. Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches. Interviews with stakeholders can be a useful method for identifying user needs and establishing requirements. However, interviews are also problematic. They are time consuming and may result in insufficient, irrelevant or invalid data. Our goal is to re-examine the methodology of interview design, to determine how various contextual factors affect the success of interviews in requirements engineering. We present a case study of a Web conferencing system used by a support group for spousal caregivers of people with dementia. Two sets of interviews were conducted to identify requirements for a new version of the system. Both sets of interviews had the same information elicitation goals, but each used different interview tactics. A comparison of the participants' responses to each format offers insights into the relationship between the interview context and the relative success of each interview technique for eliciting the desired information. As a result of what we learned, we propose a framework to help analysts design interviews and chose tactics based on the context of the elicitation process. We call this the contextual risk analysis framework. We report on our design of Curated City, a website that lets people build their own personal guide to the city's neighborhoods by chronicling their favorite experiences. Although users make their own personal guides, they are immersed in a social curatorial experience where they are influenced directly and indirectly by the guides of others. We use a 2-week field trial involving 20 residents of Pittsburgh as a technological probe to explore the initial design decisions, and we further refine the design landscape through subject interviews. Based on this study, we identify a set of design recommendations for building scalable social platforms for curating the experiences of the city. 1. Introduction 2. The news interview in context 3. Framing the news interview 4. Taking turns and 'doing' news interview talk 5. Defensible questioning 6. Designing questions 7. Answers and evasions 8. The panel interview 9. Conclusion: the future of the news interview. In complex conversation tasks, people react to their interlocutor’s state, such as uncertainty and engagement to improve conversation effectiveness Forbes-Riley and Litman (Adapting to student uncertainty improves tutoring dialogues, pp 33–40, 2009 [2]). If a conversational system reacts to a user’s state, would that lead to a better conversation experience? To test this hypothesis, we designed and implemented a dialog system that tracks and reacts to a user’s state, such as engagement, in real time. We designed and implemented a conversational job interview task based on the proposed framework. The system acts as an interviewer and reacts to user’s disengagement in real-time with positive feedback strategies designed to re-engage the user in the job interview process. Experiments suggest that users speak more while interacting with the engagement-coordinated version of the system as compared to a non-coordinated version. Users also reported the former system as being more engaging and providing a better user experience. Requirements elicitation is the software engineering activity in which stakeholder needs are understood. It involves identifying and prioritizing requirements-a process difficult to scale to large software projects with many stakeholders. This paper proposes StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritize requirements in large software projects. StakeRare identifies stakeholders and asks them to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritizes stakeholders using a variety of social network measures to determine their project influence. It then asks the stakeholders to rate an initial list of requirements, recommends other relevant requirements to them using collaborative filtering, and prioritizes their requirements using their ratings weighted by their project influence. StakeRare was evaluated by applying it to a software project for a 30,000-user system, and a substantial empirical study of requirements elicitation was conducted. Using the data collected from surveying and interviewing 87 stakeholders, the study demonstrated that StakeRare predicts stakeholder needs accurately and arrives at a more complete and accurately prioritized list of requirements compared to the existing method used in the project, taking only a fraction of the time. Modern enterprises are replete with numerous online processes. Many must be performed frequently and are tedious, while others are done less frequently yet are complex or hard to remember. We present interviews with knowledge workers that reveal a need for mechanisms to automate the execution of and to share knowledge about these processes. In response, we have developed the CoScripter system (formerly Koala [11]), a collaborative scripting environment for recording, automating, and sharing web-based processes. We have deployed CoScripter within a large corporation for more than 10 months. Through usage log analysis and interviews with users, we show that CoScripter has addressed many user automation and sharing needs, to the extent that more than 50 employees have voluntarily incorporated it into their work practice. We also present ways people have used CoScripter and general issues for tools that support automation and sharing of how-to knowledge. In order to improve the recognition accuracy of the Depression Classification Sub-Challenge (DCC) of the AVEC 2016, in this paper we propose a decision tree for depression classification. The decision tree is constructed according to the distribution of the multimodal prediction of PHQ-8 scores and participants' characteristics (PTSD Depression Diagnostic, sleep-status, feeling and personality) obtained via the analysis of the transcript files of the participants. The proposed gender specific decision tree provides a way of fusing the upper level language information with the results obtained using low level audio and visual features. Experiments are carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) database, results show that the proposed depression classification schemes obtain very promising results on the development set, with F1 score reaching 0.857 for class depressed and 0.964 for class not depressed. Despite of the over-fitting problem in training the models of predicting the PHQ-8 scores, the classification schemes still obtain satisfying performance on the test set. The F1 score reaches 0.571 for class depressed and 0.877 for class not depressed, with the average 0.724 which is higher than the baseline result 0.700. A Cognitive Task Analysis (CTA) was performed to investigate the workflow, decision processes, and cognitive demands of information assurance (IA) analysts responsible for defending against attacks on critical computer networks. We interviewed and observed 41 IA analysts responsible for various aspects of cyber defense in seven organizations within the US Department of Defense (DOD) and industry. Results are presented as workflows of the analytical process and as attribute tables including analyst goals, decisions, required knowledge, and obstacles to successful performance. We discuss how IA analysts progress through three stages of situational awareness and how visual representations are likely to facilitate cyber defense situational awareness. This paper reports a systematic review of empirical studies concerning the effectiveness of elicitation techniques, and the subsequent aggregation of empirical evidence gathered from those studies. The most significant results of the aggregation process are as follows: (1) Interviews, preferentially structured, appear to be one of the most effective elicitation techniques; (2) Many techniques often cited in the literature, like card sorting, ranking or thinking aloud, tend to be less effective than interviews; (3) Analyst experience does not appear to be a relevant factor; and (4) The studies conducted have not found the use of intermediate representations during elicitation to have significant positive effects. It should be noted that, as a general rule, the studies from which these results were aggregated have not been replicated, and therefore the above claims cannot be said to be absolutely certain. However, they can be used by researchers as pieces of knowledge to be further investigated and by practitioners in development projects, always taking into account that they are preliminary findings. In this paper, we present results from an online survey with 1,510 participants and an interview study with 31 participants on (secure) mobile instant messaging. Our goal was to uncover how much of a role security and privacy played in people’s decisions to use a mobile instant messenger. In the interview study, we recruited a balanced sample of ITsecurity experts and non-experts, as well as an equal split of users of mobile instant messengers that are advertised as being more secure and or private (e.g., Threema) than traditional mobile IMs. Our results suggest that peer influence is what primarily drives people to use a particular mobile IM, even for secure private IMs, and that security and privacy play minor roles. A goal for the creation and improvement of music recommendation is to retrieve users' preferences and select the music adapting to the preferences. Although the existing researches achieved a certain degree of success and inspired future researches to get more progress, problem of the cold start recommendation and the limitation to the similar music have been pointed out. Hence we incorporate concept of serendipity using 'renso' alignments over Linked Data to satisfy the users' music playing needs. We first collect music-related data from Last.fm, Yahoo! Local, Twitter and LyricWiki, and then create the 'renso' relation on the Music Linked Data. Our system proposes a way of finding suitable but novel music according to the users' contexts. Finally, preliminary experiments confirm balance of accuracy and serendipity of the music recommendation. Cold start is one of the most challenging problems in recommender systems. In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL. Specifically, we use a factorization model to capture fine-grained user-item context. Then, in order to build a model that is able to boost the recommendation performance by leveraging the context, we propose a semi-supervised ensemble learning algorithm. The algorithm constructs different (weak) prediction models using examples with different contexts and then employs the co-training strategy to allow each (weak) prediction model to learn from the other prediction models. The method has several distinguished advantages over the standard recommendation methods for addressing the cold-start problem. First, it defines a fine-grained context that is more accurate for modeling the user-item preference. Second, the method can naturally support supervised learning and semi-supervised learning, which provides a flexible way to incorporate the unlabeled data. The proposed algorithms are evaluated on two real-world datasets. The experimental results show that with our method the recommendation accuracy is significantly improved compared to the standard algorithms and the cold-start problem is largely alleviated. In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as “deep jointly informed neural networks” (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets. A recommender system aims to recommend items that a user is interested in among many items. The need for the recommender system has been expanded by the information explosion. Various approaches have been suggested for providing meaningful recommendations to users. One of the proposed approaches is to consider a recommender system as a Markov decision process (MDP) problem and try to solve it using reinforcement learning (RL). However, existing RL-based methods have an obvious drawback. To solve an MDP in a recommender system, they encountered a problem with the large number of discrete actions that bring RL to a larger class of problems. In this paper, we propose a novel RL-based recommender system. We formulate a recommender system as a gridworld game by using a biclustering technique that can reduce the state and action space significantly. Using biclustering not only reduces space but also improves the recommendation quality effectively handling the cold-start problem. In addition, our approach can provide users with some explanation why the system recommends certain items. Lastly, we examine the proposed algorithm on a real-world dataset and achieve a better performance than the widely used recommendation algorithm. Multiple identity systems involve a physical self and a set of virtual identities.An 8-item model of avatar functions within a multiple identity system was identified.The vast majority of participants noted all or most functions in their interviews.The combined physical and virtual selves form a wider system of personality. Prior research has shown that approximately 50 of active participants in the 3D virtual world of Second Life have one or more secondary avatars or "alts" in addition to their primary avatar. Thus, these individuals are operating a "multiple or poly-identity system" composed of a physical self, a primary avatar, and one or more alts. However, little is known about the functions these virtual identities serve for the virtual-world user. The current study involved qualitative analysis of semistructured interviews with Second Life participants (N=24) who had a primary avatar and at least one alt. Interviews were coded to examine the functions that primary avatars and alts served. Eight functions-seven suggested by previous research on virtual world identity and one that emerged from analyses-were reflected in a large majority of the transcribed interviews and are described in the article. The current findings add to our understanding of how multifaceted identity systems operate, as more individuals augment their physical self with a set of virtual identities. We report preliminary results of our ongoing field study of IT professionals who are involved in security management. We interviewed a dozen practitioners from five organizations to understand their workplace and tools. We analyzed the interviews using a variation of Grounded Theory and predesigned themes. Our results suggest that the job of IT security management is distributed across multiple employees, often affiliated with different organizational units or groups within a unit and responsible for different aspects of it. The workplace of our participants can be characterized by their responsibilities, goals, tasks, and skills. Three skills stand out as significant in the IT security management workplace: inferential analysis, pattern recognition, and bricolage. Android's permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application's permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17 of participants paid attention to permissions during installation, and only 3 of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.