Information Retrieval Journal最新文献

In this paper, we formulate a novel Point-of-interest (POI) recommendation task to recommend a set of new POIs for visit in a short period following recent check-ins, named short-term POI recommendation. It differs from previously studied tasks and poses new challenges, such as modeling high-order POI transitions in a short period. We present PTWLR, a personalized time-weighted latent ranking model that jointly learns short-term POI transitions and user preferences with our proposed temporal weighting scheme to capture the temporal context of transitions. We extend our model to accommodate the transition dependencies on multiple recent check-ins. In experiments on real-world datasets, our model consistently outperforms seven widely used methods by significant margins in various contexts, demonstrating its effectiveness on our task. Further analysis shows that all proposed components contribute to performance improvement.

As an auto-parallelization technique with the level of thread on multi-core, Thread-Level Speculation (TLS) which is also called Speculative Multithreading (SpMT), partitions programs into multiple threads and speculatively executes them under conditions of ambiguous data and control dependence. Thread partitioning approach plays a key role to the performance enhancement in TLS. The existing heuristic rules-based approach (HR-based approach) which is an one-size-fits-all strategy, can not guarantee to achieve the satisfied thread partitioning. In this paper, an importance degree based thread partitioning approach (IDaTPA) is proposed to realize the partition of irregular programs into multithreads. IDaTPA implements biasing partitioning for every procedure with a machine learning method. It mainly includes: constructing sample set, expression of knowledge, calculation of similarity, prediction model and the partition of the irregular programs is performed by the prediction model. Using IDaTPA, the subprocedures in unseen irregular programs can obtain their satisfied partition. On a generic SpMT processor (called Prophet) to perform the performance evaluation for multithreaded programs, the IDaTPA is evaluated and averagely delivers a speedup of 1.80 upon a 4-core processor. Furthermore, in order to obtain the portability evaluation of IDaTPA, we port IDaTPA to 8-core processor and obtain a speedup of 2.82 on average. Experiment results show that IDaTPA obtains a significant speedup increasement and Olden benchmarks respectively deliver a 5.75% performance improvement on 4-core and a 6.32% performance improvement on 8-core, and SPEC2020 benchmarks obtain a 38.20% performance improvement than the conventional HR-based approach.

With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart contracts and blockchain transactions has attracted more and more attention. In particular, front-running attacks on the Ethereum platform have become a major security concern. These attack strategies exploit the transparency and certainty of the blockchain, enabling attackers to gain unfair economic benefits by manipulating the transaction order. This study proposes a sandwich attack detection system integrated into the go-Ethereum client (Geth). This system, by analyzing transaction data streams, effectively detects and defends against front-running and sandwich attacks. It achieves real-time analysis of transactions within blocks, quickly and effectively identifying abnormal patterns and potential attack behaviors. The system has been optimized for performance, with an average processing time of 0.442 s per block and an accuracy rate of 83%. Response time for real-time detection new blocks is within 5 s, with the majority occurring between 1 and 2 s, which is considered acceptable. Research findings indicate that as a part of the go-Ethereum client, this detection system helps enhance the security of the Ethereum blockchain, contributing to the protection of DeFi users’ private funds and the safety of smart contracts. The primary contribution of this study lies in offering an efficient blockchain transaction monitoring system, capable of accurately detecting sandwich attack transactions within blocks while maintaining normal operation speeds as a full node.

This paper introduces the Collection on ECIR 2023. The 45th European Conference on Information Retrieval (ECIR 2023) was held in Dublin, Ireland, during April 2–6, 2023. The conference was the largest ECIR ever, and brought together hundreds of researchers from Europe and abroad. A selection of papers shortlisted for the best paper awards was asked to submit expanded versions appearing in this Discover Computing (formerly the Information Retrieval Journal) Collection on ECIR 2023. First, an analytic paper on incorporating first stage retrieval status values as input in neural cross-encoder re-rankers. Second, new models and new data for a new task of temporal natural language inference. Third, a weak supervision approach to video retrieval overcoming the need for large-scale human labeled training data. Together, these papers showcase the breadth and diversity of current research on information retrieval.

It is useful for machines to know whether text information remains valid or not for various applications including text comprehension, story understanding, temporal information retrieval, and user state tracking on microblogs as well as via chatbot conversations. This kind of inference is still difficult for current models, including also large language models, as it requires temporal commonsense knowledge and reasoning. We approach in this paper the task of Temporal Validity Reassessment, inspired by traditional natural language reasoning to determine the updates of the temporal validity of text content. The task requires judgment whether actions expressed in a sentence are still ongoing or rather completed, hence, whether the sentence still remains valid or has become obsolete, given the presence of context in the form of a supplementary content such as a follow-up sentence. We first construct our own dataset for this task and train several machine learning models. Then we propose an effective method for learning information from an external knowledge base that gives information regarding temporal commonsense knowledge. Using our prepared dataset, we introduce a machine learning model that incorporates the information from the knowledge base and demonstrate that incorporating external knowledge generally improves the results. We also experiment with different embedding types to represent temporal commonsense knowledge as well as with data augmentation methods to increase the size of our dataset.

BCubed is a mathematically clean, elegant and intuitively well behaved external performance metric for clustering tasks. BCubed compares a predicted clustering to a known ground truth clustering through elementwise precision and recall scores. For each element, the predicted and ground truth clusters containing the element are compared, and the mean over all elements is taken. We argue that BCubed overestimates performance, for the intuitive reason that the clustering gets credit for putting an element into its own cluster. This is repaired, and we investigate the repaired version, called “Elements Like Me (ELM)”. We extensively evaluate ELM from both a theoretical and empirical perspective, and conclude that it retains all of its positive properties, and yields a minimum zero score when it should. Synthetic experiments show that ELM can produce different rankings of predicted clusterings when compared to BCubed, and that the ELM scores are distributed with lower mean and a larger variance than BCubed.

The requirement that, for fair document retrieval, the documents should be ranked in the order to equally expose authors and organizations has been studied for some years. The fair exposure of a ranking, however, undermines the optimality of the Probability Ranking Principle and as a consequence retrieval effectiveness. It is shown how the variations of fairness and effectiveness can be related by a model. To this end, the paper introduces a fairness measure inspired in Gini’s index of mutability for non-ordinal variables and relates it to a general enough measure of effectiveness, thus modeling the connection between these two dimensions of Information Retrieval. The paper also introduces the measurement of the statistical significance of the fairness measure. An empirical study completes the paper.

Abstract

E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these modern retrieval systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such a form. These natural language product retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially-matched relevant results that could be of interest to the customers. This undesirable effect costs e-commerce vendors to lose sales on their merchandise. In solving this problem, we propose a novel product retrieval system, called ({textit{QuePR}}) , that parses arbitrarily simple and complex natural language queries with(out) Boolean operators, utilizes combinatorial numeric and content-based matching to extract relevant products from a database, and ranks retrieved resultant products by relevance before presenting them to the end-user. The advantages of ({textit{QuePR}}) are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. ({textit{QuePR}}) is unique, easy to use, and scalable to all product categories. To verify the accuracy of ({textit{QuePR}}) in retrieving relevant products on different product domains, we have conducted different performance analyses and compared ({textit{QuePR}}) with other ranking and retrieval systems. The empirical results verify that ({textit{QuePR}}) outperforms others while maintaining an optimal runtime speed.

Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

Stemming algorithms are crucial tools for enhancing the information retrieval process in natural language processing. This paper presents a novel Arabic light stemming algorithm called Tashaphyne0.4, the idea behind this algorithm is to extract the most precise ‘roots’, and ‘stems’ from words of an Arabic text. Thus, the proposed algorithm acts as rooter, stemmer, and segmentation tools at the same time. Our approach involves tri-fold phases (i.e., Preparation, Stems-Extractor, and Root-Extractor). Tashaphyne0.4 has shown better results than six other stemmers (i.e., Khoja, ISRI, Motaz/Light10, Tashaphyne0.3, FARASA, and Assem stemmers). The comparison is performed using four different Arabic comprehensive-benchmarks datasets. In conclusion, our proposed stemmer achieved remarkable results and outperformed other competitive stemmers in extracting ‘Roots’ and ‘Stems’.