A Survey on Building Recommendation Systems Using Data Mining Techniques

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

Selfish learning is more important than fair-minded conditional cooperation in public-goods games

Why does human cooperation often unravel in economic experiments despite a promising start? Previous studies have interpreted the decline as the reaction of disappointed cooperators retaliating in response to lesser cooperators (conditional cooperation). This interpretation has been considered evidence of a uniquely human form of cooperation, motivated by altruistic concerns for fairness and requiring special evolutionary explanations. However, experiments have typically shown individuals information about both their personal payoff and information about the decisions of their groupmates (social information). Showing both confounds explanations based on conditional cooperation with explanations based on individuals learning how to better play the game. Here we experimentally decouple these two forms of information, and thus these two learning processes, in public goods games involving 616 Swiss university participants. We find that payoff information leads to a greater decline, supporting a payoff-based learning hypothesis. In contrast, social information has small or negligible effect, contradicting the conditional cooperation hypothesis. We also find widespread evidence of both confusion and selfish motives, suggesting that human cooperation is maybe not so unique after all.

More of the Same? Comparing the Personalities of Ex-Spouse and New Partner after Divorce

The similarity of the Big Five personality traits of ex-spouses and new partners was examined post-divorce. The notion that divorcees replicate their partner choice (fixed-type hypothesis) was tested against the hypotheses that they learn to select a new partner with more marriage-stabilizing personality traits than their former spouse (learning hypothesis), or are constrained by marriage market forces to repartner with someone who has less stabilizing personality traits (marriage market hypothesis). Data was derived from a Flemish study that sampled divorcees from the national register. The sample consisted of 700 triads of divorcees, their ex-spouses, and their new partners. The analysis results rejected the fixed-type hypothesis and instead supported both the learning hypothesis and the marriage market hypothesis, with higher order repartnering supporting the latter. Women also seemed to validate both hypotheses, as their partner comparison showed decreases in both stabilizing traits (conscientiousness and agreeableness) and destabilizing traits (neuroticism and extraversion). Overall, the results seem to suggest that divorcees do not repartner with someone of the same personality as their ex-spouse, and they are in some cases constrained by marriage market forces to repartner with less stabilizing personalities, while in other cases they are able to improve their partner selection.

Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis

Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (Mpro) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (Mpro) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (Mpro), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.

DAILY VERBS: TEACHING ENGLISH VERB TENSES THROUGH A SIMPLE VIDEO GAME

This study introduces an educational game called “Daily Verbs” which teaches verb tenses to second language learners. The mechanics of the game is simple. The player moves a sprite through daily tasks and to each task there is a sentence attached stating the relevant tense. In the game, the researcher made use of Stephen Krashen’s principles of second language acquisition, namely, the acquisition vs. learning hypothesis, the affective filter, and the monitor hypothesis. Their manifestation in the game is explained in this study. Besides, the researcher made extensive use of John Gee’s 13 principles of learning embedded in video games. Their evidence in the game is also explained in the study. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0871/a.php" alt="Hit counter" /></p>

Vocal learning as a preadaptation for the evolution of human beat perception and synchronization

The human capacity to synchronize movements to an auditory beat is central to musical behaviour and to debates over the evolution of human musicality. Have humans evolved any neural specializations for music processing, or does music rely entirely on brain circuits that evolved for other reasons? The vocal learning and rhythmic synchronization hypothesis proposes that our ability to move in time with an auditory beat in a precise, predictive and tempo-flexible manner originated in the neural circuitry for complex vocal learning. In the 15 years since the hypothesis was proposed a variety of studies have supported it. However, one study has provided a significant challenge to the hypothesis. Furthermore, it is increasingly clear that vocal learning is not a binary trait animals have or lack, but varies more continuously across species. In the light of these developments and of recent progress in the neurobiology of beat processing and of vocal learning, the current paper revises the vocal learning hypothesis. It argues that an advanced form of vocal learning acts as a preadaptation for sporadic beat perception and synchronization (BPS), providing intrinsic rewards for predicting the temporal structure of complex acoustic sequences. It further proposes that in humans, mechanisms of gene-culture coevolution transformed this preadaptation into a genuine neural adaptation for sustained BPS. The larger significance of this proposal is that it outlines a hypothesis of cognitive gene-culture coevolution which makes testable predictions for neuroscience, cross-species studies and genetics. This article is part of the theme issue ‘Synchrony and rhythm interaction: from the brain to behavioural ecology’.

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. In this article, we use computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs. We hypothesize that infants can assign basic semantic features, such as “is-an-object” and/or “is-an-action,” to the very first words they learn, then use these words, the semantic seed, to ground proto-categories of nouns and verbs. The contexts in which these words occur, would then be exploited to bootstrap the noun and verb categories: unknown words are attributed to the class that has been observed most frequently in the corresponding context. To test our hypothesis, we designed a series of computational experiments which used French corpora of child-directed speech and different sizes of semantic seed. We partitioned these corpora in training and test sets: the model extracted the two-word contexts of the seed from the training sets, then used them to predict the syntactic category of content words from the test sets. This very simple algorithm demonstrated to be highly efficient in a categorization task: even the smallest semantic seed (only 8 nouns and 1 verb known) yields a very high precision (~90% of new nouns; ~80% of new verbs). Recall, in contrast, was low for small seeds, and increased with the seed size. Interestingly, we observed that the contexts used most often by the model featured function words, which is in line with what we know about infants' language development. Crucially, for the learning method we evaluated here, all initialization hypotheses are plausible and fit the developmental literature (semantic seed and ability to analyse contexts). While this experiment cannot prove that this learning mechanism is indeed used by infants, it demonstrates the feasibility of a realistic learning hypothesis, by using an algorithm that relies on very little computational and memory resources. Altogether, this supports the idea that a probabilistic, context-based mechanism can be very efficient for the acquisition of syntactic categories in infants.

Corporate Social Responsibility as a managerial learning process

The purpose of this paper is twofold. 1) We propose for the first time in the literature a theory (managerial learning hypothesis) that may explain why managers engage in corporate social responsibility (CSR). 2) We use an intuitive empirical methodology (Edmans et al. 2017) to test the relevance/irrelevance of our new theory. The idea behind our main contribution is that managers engage in CSR to learn new relevant information from other informed stakeholders. In return, managers will use both the new information and other information they already have to choose the optimal level of firm’s investment (Jayaraman and Wu, 2019). Therefore, we propose to examine whether a strong CSR engagement improves revelatory efficiency (Edmans et al. 2012, 2017). The latter accounts for the extent to which stock prices reveal new information to managers that will help them make value-maximizing choices. Our findings suggest that CSR activities do not allow firm’s managers to extract new information from their stock prices and ultimately improve the efficiency of their investment choices.

The zone of latent solutions and its relevance to understanding ape cultures

The zone of latent solutions (ZLS) hypothesis provides an alternative approach to explaining cultural patterns in primates and many other animals. According to the ZLS hypothesis, non-human great ape (henceforth: ape) cultures consist largely or solely of latent solutions. The current competing (and predominant) hypothesis for ape culture argues instead that at least some of their behavioural or artefact forms are copied through specific social learning mechanisms (“copying social learning hypothesis”) and that their forms may depend on copying (copying-dependent forms). In contrast, the ape ZLS hypothesis does not require these forms to be copied. Instead, it suggests that several (non-form-copying) social learning mechanisms help determine the frequency (but typically not the form) of these behaviours and artefacts within connected individuals. The ZLS hypothesis thus suggests that increases and stabilisations of a particular behaviour’s or artefact’s frequency can derive from socially-mediated (cued) form reinnovations. Therefore, and while genes and ecology play important roles as well, according to the ape ZLS hypothesis, apes typically acquire the forms of their behaviours and artefacts individually, but are usually socially induced to do so (provided sufficient opportunity, necessity, motivation and timing). The ZLS approach is often criticized—perhaps also because it challenges the current null hypothesis, which instead assumes a requirement of form-copying social learning mechanisms to explain many ape behavioural (and/or artefact) forms. However, as the ZLS hypothesis is a new approach, with less accumulated literature compared to the current null hypothesis, some confusion is to be expected. Here, we clarify the ZLS approach—also in relation to other competing hypotheses—and address misconceptions and objections. We believe that these clarifications will provide researchers with a coherent theoretical approach and an experimental methodology to examine the necessity of form-copying variants of social learning in apes, humans and other species.