Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

On n-absorbing ideals and (m,n)-closed ideals in trivial ring extensions of commutative rings

Let [Formula: see text] be a commutative ring with [Formula: see text]. Recall that a proper ideal [Formula: see text] of [Formula: see text] is called a 2-absorbing ideal of [Formula: see text] if [Formula: see text] and [Formula: see text], then [Formula: see text] or [Formula: see text] or [Formula: see text]. A more general concept than 2-absorbing ideals is the concept of [Formula: see text]-absorbing ideals. Let [Formula: see text] be a positive integer. A proper ideal [Formula: see text] of [Formula: see text] is called an n-absorbing ideal of [Formula: see text] if [Formula: see text] and [Formula: see text], then there are [Formula: see text] of the [Formula: see text]’s whose product is in [Formula: see text]. The concept of [Formula: see text]-absorbing ideals is a generalization of the concept of prime ideals (note that a prime ideal of [Formula: see text] is a 1-absorbing ideal of [Formula: see text]). Let [Formula: see text] and [Formula: see text] be integers with [Formula: see text]. A proper ideal [Formula: see text] of [Formula: see text] is called an [Formula: see text]-closed ideal of [Formula: see text] if whenever [Formula: see text] for some [Formula: see text] implies [Formula: see text]. Let [Formula: see text] be a commutative ring with [Formula: see text] and [Formula: see text] be an [Formula: see text]-module. In this paper, we study [Formula: see text]-absorbing ideals and [Formula: see text]-closed ideals in the trivial ring extension of [Formula: see text] by [Formula: see text] (or idealization of [Formula: see text] over [Formula: see text]) that is denoted by [Formula: see text].

Optimizing Warnings on E-Cigarette Advertisements

Introduction We examined the effect of visual optimizations on warning text recall. Methods We used Amazon’s Mechanical Turk to recruit 1854 young adult (18–34 years) electronic cigarette (e-cigarette) users or susceptible nonusers. We conducted a between-subjects 3 × 2 × 2 experiment to examine the influence of color (black text on white background [BW] vs. black on yellow [BY] vs. yellow on black [YB]), shape (rectangle vs. novel), and signal word (presence vs. absence of the word “warning”). We randomized participants to view one of 12 warnings on a fictional e-cigarette advertisement. We coded open-ended recall responses into three categories: (1) recalled nothing, (2) recalled something, (3) recalled the concept. We examined main effects on warning text recall using multinomial regression. We examined differences in attention, perceived message effectiveness, and appeal. Results Those exposed to BW or BY warnings were more likely than those exposed to YB to recall something (AOR = 1.6, AOR = 1.5, respectively) or the concept (OR = 1.4, BW). Those exposed to novel shape (44.7% novel vs. 37.9% rectangle; p = .003) or color (44.5% BY vs. 41.9% YB vs. 37.5% BW; p = .04) warnings were more likely to report attention to the warning. In aided recall, those exposed to the signal word were more likely than those not exposed to select the correct response (64.0% vs. 31.3%; p < .0001). We did not find differences for message effectiveness or appeal. Conclusions Visual optimizations such as color may influence warning text recall and should be considered for new warnings. Research should continue exploring variations for advertisement warnings to maximize attention to warning text. Implications This study examines the impact of visual optimizations on recall of the US Food and Drug Administration-mandated e-cigarette advertisement warning text. We found that color might influence warning text recall, but we did not find effects for shape or signal word. It is possible the newly mandated e-cigarette advertisement warnings, which are required to occupy at least 20% of the advertisement, are currently novel enough to attract attention. Future research should examine optimizations following implementation of the new advertisement warnings.

The Effects of Orienting Instructions on Readers’ Ability to Recall Literal Information from Expository Texts

This study aimed to examine whether instructing readers to judge text information can impair or facilitate their ability to recall information from expository texts of different genres. Experiment One used four expository texts and examined three types of orienting instructions: To answer pre-text questions, to be prepared to answer questions after the reading the text, to be prepared to judge the knowledge introduced in the text. The results of the study indicated that the use of pre-text questions did not improve readers’ performance in a recall test; in fact, they impaired the readers’ overall recall ability. However, being forewarned that they would be asked to provide a judgment on the information contained within a passage after reading it did enhance the participants’ ability to recall information from expository texts. Experiment 2 used the same four expository texts and examined the relationship between text recall and participants’ judgement of text information with particular cognitive abilities. The results reveal variations in the patterns of correlations between recall and rating of the text and other cognitive factors across the different text genres. The outcomes and implications of this research are discussed in this paper.

Effects of gloss type on text recall and incidental vocabulary learning in mobile-assisted L2 listening

This study investigates the effects of multimedia glosses on text recall and incidental vocabulary learning in a mobile-assisted L2 listening task. A total of 88 participants with a low level of proficiency in English were randomly assigned to one of four conditions that involved single channel (textual-only, pictorial-only) and dual-channel (textual-plus-pictorial) glosses as well as a control condition where no glosses were provided. The participants listened to a story through their mobile phones and were engaged in an immediate free recall task and unannounced vocabulary tests after listening. The findings indicated that access to glosses facilitated recognition and production of vocabulary with the type of gloss having no effect. On the other hand, glosses had no effect on text recall.

On (m,n)-closed ideals of commutative rings

Let [Formula: see text] be a commutative ring with [Formula: see text], and let [Formula: see text] be a proper ideal of [Formula: see text]. Recall that [Formula: see text] is an [Formula: see text]-absorbing ideal if whenever [Formula: see text] for [Formula: see text], then there are [Formula: see text] of the [Formula: see text]’s whose product is in [Formula: see text]. We define [Formula: see text] to be a semi-[Formula: see text]-absorbing ideal if [Formula: see text] for [Formula: see text] implies [Formula: see text]. More generally, for positive integers [Formula: see text] and [Formula: see text], we define [Formula: see text] to be an [Formula: see text]-closed ideal if [Formula: see text] for [Formula: see text] implies [Formula: see text]. A number of examples and results on [Formula: see text]-closed ideals are discussed in this paper.

Graded A-identities for the matrix algebra of order two

Let [Formula: see text] be a field of characteristic 0 and let [Formula: see text]. The algebra [Formula: see text] admits a natural grading [Formula: see text] by the cyclic group [Formula: see text] of order 2. In this paper, we describe the [Formula: see text]-graded A-identities for [Formula: see text]. Recall that an A-identity for an algebra is a multilinear polynomial identity for that algebra which is a linear combination of the monomials [Formula: see text] where [Formula: see text] runs over all even permutations of [Formula: see text] that is [Formula: see text], the [Formula: see text]th alternating group. We first introduce the notion of an A-identity in the case of graded polynomials, then we describe the graded A-identities for [Formula: see text], and finally we compute the corresponding graded A-codimensions.