Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.Comment: TOIS 202

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems

This paper investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We jointly design the radar waveform and communication beamforming vector at the two systems, such that the interference between the base station (BS) and radar is reduced, and the detrimental radar interference to the communication system is enhanced to jam the eavesdropper, thereby increasing secure information transmission performance. In particular, by considering the imperfect channel state information (CSI) for the user and eavesdropper, we maximize the worst-case secrecy rate at the user, while ensuring the detection performance of radar system. To tackle this challenging problem, we propose a two-layer robust cooperative algorithm based on the S-lemma and semidefinite relaxation techniques. Simulation results demonstrate that the proposed algorithm achieves significant secrecy rate gains over the non-robust scheme. Furthermore, we illustrate the trade-off between secrecy rate and detection probability

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction. To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.Comment: 11 pages, 9 figure

Bibliometric analysis of kinship analysis from 1960 to 2023: global trends and development

Kinship analysis is a crucial aspect of forensic genetics. This study analyzed 1,222 publications on kinship analysis from 1960 to 2023 using bibliometric analysis techniques, investigating the annual publication and citation patterns, most productive countries, organizations, authors and journals, most cited documents and co-occurrence of keywords. The initial publication in this field occurred in 1960. Since 2007, there has been a significant increase in publications, with over 30 published annually except for 2010. China had the most publications (n = 213, 17.43%), followed by the United States (n = 175, 14.32%) and Germany (n = 89, 7.28%). The United States also had the highest citation count. Sichuan University in China has the largest number of published articles. The University of Leipzig and the University of Cologne in Germany exhibit the highest total citation count and average citation, respectively. Budowle B was the most prolific author and Kayser M was the most cited author. In terms of publications, Forensic Science International-Genetics, Forensic Science International, and International Journal of Legal Medicine were the most prolific journals. Among them, Forensic Science International-Genetics boasted the highest h-index, citation count, and average citation rate. The most frequently cited publication was “Van Oven M, 2009, Hum Mutat”, with a total of 1,361 citations. The most frequent co-occurrence keyword included “DNA”, “Loci”, “Paternity testing”, “Population”, “Markers”, and “Identification”, with recent interest focusing on “Kinship analysis”, “SNP” and “Inference”. The current research is centered around microhaplotypes, forensic genetic genealogy, and massively parallel sequencing. The field advanced with new DNA analysis methods, tools, and genetic markers. Collaborative research among nations, organizations, and authors benefits idea exchange, problem-solving efficiency, and high-quality results

An efficient synthesis of Vildagliptin intermediates

Efficient and high yielding methods for the preparation of vildagliptin 1 intermediate of (S)-1-(2-chloroacetyl) pyrrolidine-2-carbonitrile 2 and 3-amino-1-adamantane alcohol 3 respectively have been described. (S)-1-(2-Chloroacetyl) pyrrolidine-2-carbonitrile 2 has been synthesized from l-proline 2a via chloroacetyl chloride, performed with acetonitrile in the presence of sulfuric acid via one-pot reactions. 3-Amino-1-adamantane alcohol 3 has been prepared from amantadine hydrochloride via oxidation by sulfuric acid/nitric acid and boric acid as catalyst, and has been subjected to ethanol extraction. The overall yield is about 95%.

An efficient synthesis of Vildagliptin intermediates

1128-1131Efficient and high yielding methods for the preparation of vildagliptin 1 intermediate of (S)-1-(2-chloroacetyl) pyrrolidine-2-carbonitrile 2 and 3-amino-1-adamantane alcohol 3 respectively have been described. (S)-1-(2-Chloroacetyl) pyrrolidine-2-carbonitrile 2 has been synthesized from L-proline 2a via chloroacetyl chloride, performed with acetonitrile in the presence of sulfuric acid via one-pot reactions. 3-Amino-1-adamantane alcohol 3 has been prepared from amantadine hydrochloride via oxidation by sulfuric acid/nitric acid and boric acid as catalyst, and has been subjected to ethanol extraction. The overall yield is about 95%