Set Of Related Items Research Articles

Automated Category Tree Construction: Hardness Bounds and Algorithms

Category trees, or taxonomies, are rooted trees where each node, called a category, corresponds to a set of related items. The construction of taxonomies has been studied in various domains, including e-commerce, document management, and question answering. Multiple algorithms for automating construction have been proposed, employing a variety of clustering approaches and crowdsourcing. However, no formal model to capture such categorization problems has been devised, and their complexity has not been studied. To address this, we propose in this work a combinatorial model that captures many practical settings and show that the aforementioned empirical approach has been warranted, as we prove strong inapproximability bounds for various problem variants and special cases when the goal is to produce a categorization of the maximum utility. In our model, the input is a set of n weighted item sets that the tree would ideally contain as categories. Each category, rather than perfectly match the corresponding input set, is allowed to exceed a given threshold for a given similarity function. The goal is to produce a tree that maximizes the total weight of the sets for which it contains a matching category. A key parameter is an upper bound on the number of categories an item may belong to, which produces the hardness of the problem, as initially each item may be contained in an arbitrary number of input sets. For this model, we prove inapproximability bounds, of order \(\tilde{\Theta }(\sqrt {n}) \) or \(\tilde{\Theta }(n) \) , for various problem variants and special cases, loosely justifying the aforementioned heuristic approach. Our work includes reductions based on parameterized randomized constructions that highlight how various problem parameters and properties of the input may affect the hardness. Moreover, for the special case where the category must be identical to the corresponding input set, we devise an algorithm whose approximation guarantee depends solely on a more granular parameter, allowing improved worst-case guarantees, as well as the application of practical exact solvers. We further provide efficient algorithms with much improved approximation guarantees for practical special cases where the cardinalities of the input sets or the number of input sets each items belongs to are not too large. Finally, we also generalize our results to DAG-based and non-hierarchical categorization.

Enhancing Item-level Bundle Representation for Bundle Recommendation

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this paper, we propose a novel approach EBRec , short of E nhanced B undle Rec ommendation, which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to enhance the previous bundle representation that solely relies on the bundle-item affiliation information. Second, we further enhance the B-U-I correlations by augmenting the observed user-item interactions with interactions generated from pre-trained models, thus improving the item-level bundle representations. We conduct extensive experiments on three public datasets, and the results justify the effectiveness of our approach as well as the two core modules. Codes and datasets are available at https://github.com/answermycode/EBRec.

Is assessment of oral health-related quality of life burdensome? An item nonresponse analysis of the oral health impact profile

AimThis study aimed to investigate if in the 49-item Oral Health Impact Profile (OHIP): (i) more missing data occurred when participants answered more questions, (ii) more missing data occurred in a particular item or set of related items, and (iii) item missingness was associated with the demographic characteristics and oral health-related quality of life (OHRQoL) impairment level.MethodsWe used OHIP data from the Dimensions of OHRQoL (DOQ) project, which consolidated data from 35 individual studies. Among these studies, we analyzed OHIP data from 19 studies (4,847 surveyed individuals, of which 3,481 were completed under supervision and 1,366 were completed unsupervised) that contained some missing information. We computed descriptive statistics to investigate the OHIP missingness. We also used logistic regression analyses, with missing information as the dependent variable, and number of questions filled in (OHIP item rank) as the independent variable for samples with and without supervision. To investigate whether missing data occurs more in a particular item or set of related items we fitted regression models with individual OHIP items and the OHRQoL dimensions as indicator variables. We also investigated age, gender, and OHRQoL level as predictor variables for missing OHIP items.ResultsWe found very low levels of missingness across individual OHIP items and set of related items, and there was no particular item or set of related items that was associated with more missing data. Also, more missing data did not depend on whether the participants answered more questions. In studies without supervision, older persons and females were 5.47 and 2.66 times more likely to have missing items than younger persons and females. However, in studies with supervision, older persons, and participants with more OHRQoL impairment were 1.70 and 2.65 times more likely to have missing items.ConclusionThe study participants from general and dental patient populations did not find OHIP-49 burdensome. OHIP item missingness did not depend on a particular OHIP item or set of related items, or if the study participants responded to a greater number of OHIP items. We did not find a consistent pattern of the influence of sociodemographic and OHRQoL magnitude information on OHIP missingness. The amount of missing OHIP information was low making any potential influence likely small in magnitude.

Novel Utility Procedure for Filtering High Associated Utility Items from Transactional Databases

In data mining, mining and analysis of data from different transactional data sources is an aggressive concept to explore optimal relations between different item sets. In recent years number of algorithms/methods was proposed to mine associated rule based item sets from transactional databases. Mining optimized high utility (like profit) association rule based item sets from transactional databases is still a challenging task in item set extraction in terms of execution time. We propose High Utility based Association Pattern Growth (HUAPG) approach to explore high association utility item sets from transactional data sets based on user item sets. User related item sets to mine associated items using utility data structure (UP-tree) with respect to identification of item sets in proposed approach. Proposed approach performance with compared to hybrid and existing methods worked on synthetic related data sets. Experimental results of proposed approach not only filter candidate item sets and also reduce the run time when database contain high amount of data transactions.

Lexical Semantic Field as One of the Keys to Second Language Teaching

The article is devoted to the problem of the development of skills connected with the acquisition of foreign lexis (Lexis = all possible words or phrases in a language) on the basis of semantic fields (Semantic field = a lexical set of related items, e.g., colour, red, green, blue). This becomes possible due to grouping well-known and unknown to the students lexical units to systemized semantic groups—semantic fields. Semantic fields can be useful for the quantitative and qualitative enrichment of the vocabulary; they serve to choose a lexical unit correctly, to collocate lexical units according to existing norms, to use the lexis of various semantic fields in different contexts without mistakes, to guarantee conscious, automatic language skills of the students and to increase their active vocabulary.

Establishing the validity of English GP Patient Survey items evaluating out-of-hours care

BackgroundA 2014 national audit used the English General Practice Patient Survey (GPPS) to compare service users’ experience of out-of-hours general practitioner (GP) services, yet there is no published evidence on...

An Efficient Mining Algorithm for Determining Related Item Sets using Classification and Association Rules

In the present days, data mining is the advanced research area because it is one of the important step in the knowledge discovery process. This paper presents an experimental study of finding the frequent item sets by classifying the data base transactions into classes by using Decision tree induction based classification and applying Frequent-Pattern (FP) growth on the classes. First, data base transactions are preprocessed by using the pre-processing techniques and those are classified into classes based on information gain. After classifying the transactions into classes, we applied the FP growth algorithm to obtain the frequent or related item sets. This proposed technique is also suitable for heterogeneous data bases. We examined this technique on different types of data bases and by using this technique it have given the accuracy of 96%

Augmenting transportation-related recommendations through data mining

This paper reports on the exploitation of data mining techniques during the formulation of purposeful association rules out of the transactions' database of a transportation management system. The rules' construction is performed through an elaborated version of the AprioriTid algorithm. The proposed algorithm is generic and capable to construct such rules by creating a large set of related items. The constructed rules can be used by the system's recommender module, which is responsible for providing recommendations to the associated users. The recommendation process takes into account the constructed rules and techniques that derive from the area of Collaborative Filtering (CF).

Using ad hoc measures for response styles: a cautionary note

This research note addresses the challenge of how to optimally measure acquiescence response style (ARS) and extreme response style (ERS). This is of crucial importance in assessing results from studies that have tried to identify antecedents of response styles (such as age, education level, national culture). Using survey data from the Netherlands, a comparison is made between the traditional method and a more recently proposed method of measuring ARS and ERS (i.e., the convergent validity across both methods is assessed). The traditional method is based on an ad hoc set of related items. The alternative method uses a set of randomly sampled items to optimize heterogeneity and representativeness of the items. It is found that the traditional method may lead to response style measures that are suboptimal for estimating levels of ARS and ERS as well as relations of ARS and ERS with other variables (like hypothesized antecedents). Recommendations on how to measure response styles are provided.