Recency Information Research Articles

Because it is brand new! Recency heuristic for product innovativeness evaluation

AbstractThis research identifies recency heuristic utilized by consumers with limited prior knowledge for product innovativeness evaluation. Consumers with limited prior knowledge of a product category perceived a new product as more innovative when its release date was more recent, while consumers with prior knowledge remained uninfluenced by recency information (Study 1). The effect was replicated at the product level (Study 2). It further demonstrates two critical boundary conditions—when recency was either irrelevant information or a rationally legitimate evaluative tool, recency heuristic was inapplicable (Study 3). The present research draws attention to the role of recency in conceptualizing product innovativeness and further elaborates the understanding of how the construct of innovativeness is represented in consumers' minds by focusing on the conceptual relationship of novelty and recency. It also contributes to the heuristic literature by proposing recency as an evaluative heuristic tool for innovativeness assessment. Results provide managers with practical insight into whether to highlight or downplay product release date information depending on their target audience and the level of product innovativeness.

CARL: Compiler Assigned Reference Leasing

Data movement is a common performance bottleneck, and its chief remedy is caching. Traditional cache management is transparent to the workload: data that should be kept in cache are determined by the recency information only, while the program information, i.e., future data reuses, is not communicated to the cache. This has changed in a new cache design named Lease Cache . The program control is passed to the lease cache by a compiler technique called Compiler Assigned Reference Lease (CARL). This technique collects the reuse interval distribution for each reference and uses it to compute and assign the lease value to each reference. In this article, we prove that CARL is optimal under certain statistical assumptions. Based on this optimality, we prove miss curve convexity, which is useful for optimizing shared cache, and sub-partitioning monotonicity, which simplifies lease compilation. We evaluate the potential using scientific kernels from PolyBench and show that compiler insertions of up to 34 leases in program code achieve similar or better cache utilization (in variable size cache) than the optimal fixed-size caching policy, which has been unattainable with automatic caching but now within the potential of cache programming for all tested programs and most cache sizes.

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.

RSTrace+: Reviewer suggestion using software artifact traceability graphs

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are connected with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To make sure the review is conducted properly, the reviewer(s) should be chosen appropriately. We previously introduced a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. In this study, we introduce an advanced version of RSTrace, named RSTrace+ that accounts for recency information of traceability links including practical tool support for GitHub. In this study, we conducted a series of experiments on finding the appropriate code reviewer(s) using RSTrace+ and provided a comparison with the other code reviewer recommendation approaches. We had initially tested RSTrace+ on an open source project (Qt 3D Studio) and achieved a top-3 accuracy of 0.89 with an MRR (mean reciprocal ranking) of 0.81. In a further empirical evaluation of 40 open source projects, we compared RSTrace+ with Naive-Bayes, RevFinder and Profile based approach, and observed higher accuracies on the average. We confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches. Unlike other code reviewer recommendation approaches, RSTrace+ is not limited to recommending reviewers for source code artifacts and can potentially be used for recommending reviewers for other types of artifacts. Our approach can also visualize the affected artifacts and help the developer to make assessments of the potential impacts of change to the reviewed artifact.

Cache Replacement Schemes Based on Adaptive Time Window for Video on Demand Services in Femtocell Networks

The cache replacement policy is a crucial phase in caching-based systems that deal with the process of selecting applicable cache contents. In this paper, we propose two novel cache replacement algorithms based on the dataset obtained from a typical wireless femto network. In the first algorithm, called Weighted Least Frequently used with an Adaptive Time Window (WLF-ATW), we aim to make a balance between the network's traffic and the recognition of popular contents. The WLF-ATW algorithm takes the frequency and the recency information of files into account to ascertain the popularity of contents. We suggest another new cache replacement policy namely Fairness Scheduling-based with an Adaptive Time Window (FS-ATW) that is based on fairness scheduling in order to minimize the user's access delay. The novelty of our proposed FS-ATW lies in ranking clients according to their last situations that lead to a further user's experience. The effectiveness of these new algorithms is evaluated from the cache hit ratio, transferred byte volume, user's access delay, user's experience, and load balance. A comprehensive numerical evaluation shows that the performance of the proposed WLF-ATW and FS-ATW algorithms is significantly better than some existing cache replacement strategies.

ASA-FTL: An adaptive separation aware flash translation layer for solid state drives

• An innovative FTL approach (ASA-FTL) for identifying and separating hot/cold data at the page level based on data clustering for SSDs; • A sampling and selective caching technique to control the overhead of clustering-based hot/cold data separation approach; • A Hotness Updating Algorithm that samples and clusters data periodically to update the separation criteria and an overhead analysis of the approach; • An evaluation of the proposed approach using the FlashSim simulator with several real world workloads, showing the performance benefits of our approach compared to a current state of the art FTL; • An evaluation of the effect of the sample size and the adaptivity of the proposed ASA-FTL. The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasingly critical data-intensive applications due to its low latency (high throughput), high bandwidth, and low power consumption . Within an SSD, its Flash Translation Layer (FTL) is responsible for exposing the SSD’s flash memory storage to the computer system as a simple block device. The FTL design is one of the dominant factors determining an SSD’s lifespan and performance. To reduce the garbage collection overhead and deliver better performance, we propose a new, low-cost, adaptive separation-aware flash translation layer (ASA-FTL) that combines sampling, data clustering and selective caching of recency information to accurately identify and separate hot/cold data while incurring minimal overhead. We use sampling for light-weight identification of separation criteria, and our dedicated selective caching mechanism is designed to save the limited RAM resource in contemporary SSDs. Using simulations of ASA-FTL with both real-world and synthetic workloads, we have shown that our proposed approach reduces the garbage collection overhead by up to 28% and the overall response time by 15% compared to one of the most advanced existing FTLs. We find that the data clustering using a small sample size provides significant performance benefit while only incurring a very small computation and memory cost. In addition, our evaluation shows that ASA-FTL is able to adapt to the changes in the access pattern of workloads, which is a major advantage comparing to existing fixed data separation methods.

Enhancing Self-Controlled Learning through Recency Information in Summary Knowledge of Results

It is necessary to clarify whether motor learners choose feedback information contents related to time course of motor memory after practicing in a series of movements through self-controlled learning. The purpose of this study was to examine the role of memory issues related to primacy and recency effects on motor performance and learning ability under the self-controlled learning. Sixty female adults served as participants, and were requested to practice a sequential timing task with three movement segments of ＂N＂ shape. All participants practiced 90 trials during the acquisition phase, and 24 hours delayed retention tests were administered. Participants were categorized into either more-recency SKR (summary knowledge of results) or more-primacy SKR groups, according to the selection of relatively high recency or primacy feedback trials after the acquisition phase. The results showed that more-recency SKR group had significantly higher accuracy than that of more-primacy SKR group in both acquisition phase and retention test. Furthermore, the change score was found no significant difference. Findings from this study suggest that choosing more recency information facilitated to enhance both motor performance and learning ability of movement accuracy through self-controlled learning.

Temporal information searching behaviour and strategies

Temporal aspects have been receiving a great deal of interest in Information Retrieval and related fields. Although previous studies have proposed, designed and implemented temporal-aware systems and solutions, understanding of people’s temporal information searching behaviour is still limited. This paper reports the findings of a user study that explored temporal information searching behaviour and strategies in a laboratory setting. Information needs were grouped into three temporal classes (Past, Recency, and Future) to systematically study their characteristics. The main findings of our experiment are as follows. (1) It is intuitive for people to augment topical keywords with temporal expressions such as history, recent, or future as a tactic of temporal search. (2) However, such queries produce mixed results and the success of query reformulations appears to depend on topics to a large extent. (3) Search engine interfaces should detect temporal information needs to trigger the display of temporal search options. (4) Finding a relevant Wikipedia page or similar summary page is a popular starting point of past information needs. (5) Current search engines do a good job for information needs related to recent events, but more work is needed for past and future tasks. (6) Participants found it most difficult to find future information. Searching for domain experts was a key tactic in Future search, and file types of relevant documents are different from other temporal classes. Overall, the comparison of search across temporal classes indicated that Future search was the most difficult and the least successful followed by the search for the Past and then for Recency information. This paper discusses the implications of these findings on the design of future temporal IR systems.

The medial dorsal thalamic nucleus and the medial prefrontal cortex of the rat function together to support associative recognition and recency but not item recognition

In humans recognition memory deficits, a typical feature of diencephalic amnesia, have been tentatively linked to mediodorsal thalamic nucleus (MD) damage. Animal studies have occasionally investigated the role of the MD in single-item recognition, but have not systematically analyzed its involvement in other recognition memory processes. In Experiment 1 rats with bilateral excitotoxic lesions in the MD or the medial prefrontal cortex (mPFC) were tested in tasks that assessed single-item recognition (novel object preference), associative recognition memory (object-in-place), and recency discrimination (recency memory task). Experiment 2 examined the functional importance of the interactions between the MD and mPFC using disconnection techniques. Unilateral excitotoxic lesions were placed in both the MD and the mPFC in either the same (MD + mPFC Ipsi) or opposite hemispheres (MD + mPFC Contra group). Bilateral lesions in the MD or mPFC impaired object-in-place and recency memory tasks, but had no effect on novel object preference. In Experiment 2 the MD + mPFC Contra group was significantly impaired in the object-in-place and recency memory tasks compared with the MD + mPFC Ipsi group, but novel object preference was intact. Thus, connections between the MD and mPFC are critical for recognition memory when the discriminations involve associative or recency information. However, the rodent MD is not necessary for single-item recognition memory.

When Is the Hippocampus Involved in Recognition Memory?

The role of the hippocampus in recognition memory is controversial. Recognition memory judgments may be made using different types of information, including object familiarity, an object's spatial location, or when an object was encountered. Experiment 1 examined the role of the hippocampus in recognition memory tasks that required the animals to use these different types of mnemonic information. Rats with bilateral cytotoxic lesions in the hippocampus or perirhinal or prefrontal cortex were tested on a battery of spontaneous object recognition tasks requiring the animals to make recognition memory judgments using familiarity (novel object preference); object-place information (object-in-place memory), or recency information (temporal order memory). Experiment 2 examined whether, when using different types of recognition memory information, the hippocampus interacts with either the perirhinal or prefrontal cortex. Thus, groups of rats were prepared with a unilateral cytotoxic lesion in the hippocampus combined with a lesion in either the contralateral perirhinal or prefrontal cortex. Rats were then tested in a series of object recognition memory tasks. Experiment 1 revealed that the hippocampus was crucial for object location, object-in-place, and recency recognition memory, but not for the novel object preference task. Experiment 2 revealed that object-in-place and recency recognition memory performance depended on a functional interaction between the hippocampus and either the perirhinal or medial prefrontal cortices. Thus, the hippocampus plays a role in recognition memory when such memory involves remembering that a particular stimulus occurred in a particular place or when the memory contains a temporal or object recency component.

A Novel Cache Replacement Policy via Dynamic Adaptive Insertion and Re-Reference Prediction

Previous research illustrates that LRU replacement policy is not efficient when applications exhibit a distant re-reference interval. Recently RRIP policy is proposed to improve the performance for such kind of workloads. However, the lack of access recency information in RRIP confuses the replacement policy to make the accurate prediction. To enhance the robustness of RRIP for recency-friendly workloads, we propose an Dynamic Adaptive Insertion and Re-reference Prediction (DAI-RRP) policy which evicts data based on both re-reference prediction value and the access recency information. DAI-RRP makes adaptive adjustment on insertion position and prediction value for different access patterns, which makes the policy robust across different workloads and different phases. Simulation results show that DAI-RRP outperforms LRU and RRIP. For a single-core processor with a 1MB 16-way set last-level cache (LLC), DAI-RRP reduces CPI over LRU and Dynamic RRIP by an average of 8.1% and 2.7% respectively. Evaluations on quad-core CMP with a 4MB shared LLC show that DAI-RRP outperforms LRU and Dynamic RRIP (DRRIP) on the weighted speedup metric by an average of 8.1% and 15.7% respectively. Furthermore, compared to LRU, DAI-RRP consumes the similar hardware for 16-way cache, or even less hardware for high-associativity cache. In summary, the proposed policy is practical and can be easily integrated into existing hardware approximations of LRU.

A Multi-step Q(Λ) learning Approach to Power System Stabilizer

Power system stabilizers (PSS) are used to generate supplementary control signals for excitation system in order to damp the low frequency power system oscillation. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. This paper proposes an optimal control methodology based on reinforcement learning (RL) for damping the low frequency power system oscillations. Q(Λ) learning is a relative new model-free RL algorithm that extends the one-step Q-learning by combining it with TD(Λ) returns for general Λ in a incremental way for the delayed RL problem. The Q(Λ) learning makes use of TD(Λ) returns as the value function estimator through a trace mechanism, and additional traces appear to help bridge the gap between frequency and recency information for heuristic events. In this paper, rotor speed deviation is adopted as the state feedback input, and the voltage deviation, power deviation and rotor speed deviation are formulated as reward function. The case study shows that both of them are very helpful to enhance the small-disturbance dynamics of power system. The multi-step Q(Λ) learning can enhance convergence rate of reinforcement learning algorithm.

Recognition Memory for Objects, Place, and Temporal Order: A Disconnection Analysis of the Role of the Medial Prefrontal Cortex and Perirhinal Cortex

Recognition memory requires judgments of the previous occurrence of stimuli made on the basis of the relative familiarity of individual objects, or by integrating information concerning objects and location, or by using recency information. The present study examined the role of the medial prefrontal cortex (mPFC) and perirhinal cortex (PRH) in these distinct recognition memory processes using a series of behavioral tests: a novel object preference task, an object-in-place task, and a temporal order memory task. Also, a disconnection procedure was used to test whether these regions form components of an integrated system for recognition memory. Male DA rats received bilateral lesions in the PRH or mPFC or unilateral lesions placed in both cortices in either the same (PRH-mPFC IPSI) or contralateral (PRH-mPFC CONTRA) hemispheres. A fifth group underwent sham surgery (SHAM). In the object-in-place and temporal order memory tasks, the PRH, mPFC, and PRH-mPFC CONTRA groups were significantly impaired. However, performance in the novel object preference task was only impaired in the PRH group. No group was impaired in the object location task. These results demonstrate that the mPFC and PRH are crucial for object-in-place associational and recency discriminations, whereas the PRH but not the mPFC is important for the discrimination of novel and familiar individual objects. Importantly, these results provide direct support for the hypothesis that to make discriminations based on associational or recency information, both cortical regions operate within an integrated neural network for recognition memory.

Play it again: did this melody occur more frequently or was it heard more recently? The role of stimulus familiarity in episodic recognition of music

Episodic recognition of novel and familiar melodies was examined by asking participants to make judgments about the recency and frequency of presentation of melodies over the course of two days of testing. For novel melodies, recency judgments were poor and participants often confused the number of presentations of a melody with its day of presentation; melodies heard frequently were judged as have been heard more recently than they actually were. For familiar melodies, recency judgments were much more accurate and the number of presentations of a melody helped rather than hindered performance. Frequency judgments were generally more accurate than recency judgments and did not demonstrate the same interaction with musical familiarity. Overall, these findings suggest that (1) episodic recognition of novel melodies is based more on a generalized “feeling of familiarity” than on a specific episodic memory, (2) frequency information contributes more strongly to this generalized memory than recency information, and (3) the formation of an episodic memory for a melody depends either on the overall familiarity of the stimulus or the availability of a verbal label.

The EELRU adaptive replacement algorithm

The wide performance gap between processors and disks ensures that effective page replacement remains an important consideration in modern systems. This paper presents early eviction LRU (EELRU), an adaptive replacement algorithm. EELRU uses aggregate recency information to recognize the reference behavior of a workload and to adjust its speed of adaptation. An on-line cost/benefit analysis guides replacement decisions. This analysis is based on the LRU stack model (LRUSM) of program behavior. Essentially, EELRU is an on-line approximation of an optimal algorithm for the LRUSM. We prove that EELRU offers strong theoretical guarantees of performance relative to the LRU replacement algorithm. EELRU can never be more than a factor of 3 worse than LRU, while in a common best case it can be better than LRU by a large factor (proportional to the number of pages in memory). The goal of EELRU is to provide a simple replacement algorithm that adapts to reference patterns at all scales. Thus, EELRU should perform well for a wider range of programs and memory sizes than other algorithms. Practical experiments validate this claim. For a large number of programs and wide ranges of memory sizes, we show that EELRU outperforms LRU, typically reducing misses by 10–30%, and occasionally by much more—sometimes by a factor of 2–10. It rarely performs worse than LRU, and then only by a small amount.

LIRS

Although LRU replacement policy has been commonly used in the buffer cache management, it is well known for its inability to cope with access patterns with weak locality. Previous work, such as LRU-K and 2Q, attempts to enhance LRU capacity by making use of additional history information of previous block references other than only the recency information used in LRU. These algorithms greatly increase complexity and/or can not consistently provide performance improvement. Many recently proposed policies, such as UBM and SEQ, improve replacement performance by exploiting access regularities in references. They only address LRU problems on certain specific and well-defined cases such as access patterns like sequences and loops. Motivated by the limits of previous studies, we propose an efficient buffer cache replacement policy, called Low Inter-reference Recency Set (LIRS). LIRS effectively addresses the limits of LRU by using recency to evaluate Inter-Reference Recency (IRR) for making a replacement decision. This is in contrast to what LRU does: directly using recency to predict next reference timing. At the same time, LIRS almost retains the same simple assumption of LRU to predict future access behavior of blocks. Our objectives are to effectively address the limits of LRU for a general purpose, to retain the low overhead merit of LRU, and to outperform those replacement policies relying on the access regularity detections. Conducting simulations with a variety of traces and a wide range of cache sizes, we show that LIRS significantly outperforms LRU, and outperforms other existing replacement algorithms in most cases. Furthermore, we show that the additional cost for implementing LIRS is trivial in comparison with LRU.

Lower bounds on generalization errors for drifting rules

The problem of generalization by single-layer perceptrons is studied in the case of time-dependent rules. Lower bounds for the generalization errors within the 'single presentation of examples' case are obtained for randomly drifting rules. These bounds suggest a learning algorithm which uses knowledge of the error itself. Since this error is not readily available it has to be estimated through a mechanism of self-evaluation. The capacity of incorporating recency information into the error estimate is highly desirable. The mechanism proposed has the advantage, beyond good performance, of being self-adaptive, in the sense that it adjusts to changes in the unknown drift rate of the rule. The performance of the rule is also studied for sudden changes in an attempt to mimic the so-called Wisconsin test.