Characterisation of explicit feedback in an online music recommendation service

Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.

The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ranking models leverage implicit feedback (e.g. clicks) in order to provide the user with a personalized ranked list of recommended items. Several previous approaches have been proposed that learn from both explicit and implicit feedback to optimize the task of ranking or rating prediction at the level of recommendation algorithm. Yet we argue that these two tasks are not completely separate, but are part of a unified process: a user first interacts with a set of items and then might decide to provide explicit feedback on a subset of items. We propose to bridge the gap between the tasks of rating prediction and ranking through the use of a novel weak supervision approach that unifies both explicit and implicit feedback datasets. The key aspects of the proposed model is that (1) it is applied at the level of data pre-processing and (2) it increases the representation of less popular items in recommendations while maintaining reasonable recommendation performance. Our experimental results - on six datasets covering different types of heterogeneous user's interactions and using a wide range of evaluation metrics - show that, our proposed approach can effectively combine explicit and implicit feedback and improve the effectiveness of the baseline explicit model on the ranking task by covering a broader range of long-tail items.

Implicit feedback such as video watch time is commonly seen in many internet products. Though recommender systems with explicit feedback have been abundantly researched, there are not many methods proposed for building recommender systems with implicit feedback. Restricted Boltzmann Machine (RBM), which uses a two-layer graphical model to describe response variables through hidden units, is a promising approach. An RBM model for collaborative filtering was proposed in the literature, but it only deals with categorized ratings, which is explicit feedback. We propose a novel extension of the RBM method for collaborative filtering with implicit feedback. The model parameters can be learned efficiently through the contrastive divergence algorithm. Compared to other methods, the proposed RBM method can directly predict preferences for a new user given feedback on a few items. Also, it protects privacy by keeping user data locally and only sends updated parameters to a central server. The results on real data with several million records show it works superiorly compared to other prevalent methods.

The current study investigates the effects of more explicit versus more implicit corrective feedback on beginner Mandarin learners’ perception and production of Mandarin tones. The effects of oral corrective feedback have been extensively investigated for various domains of second language acquisition (see Mackey & Goo, 2007). For phonological errors, implicit feedback has shown to be particularly salient to learners (Mackey, Gass, & McDonough, 2000) and therefore potentially more effective for tone learning. However, for lower proficiency beginners, explicit corrective feedback has been shown to be more effective (Li, 2009, 2014). Using a mixed methods design, the current study investigated the acquisition of Mandarin tones in beginner, adult learners (n = 41) enrolled in a 14-week, one-on-one, synchronous computer-mediated communication course. Learners were divided into two experimental groups: a more implicit feedback group and a more explicit feedback group. Pretests and posttests assessed changes in tone perception and production. Upon completion of the course, learners in the more implicit feedback group had greater improvement in tone production compared to the more explicit feedback group (d = .75). Both learners and the instructor indicated a preference toward recasts for tone feedback. No statistically significant differences were found for tone perception.

E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.

In the recommendation system, compared with the explicit feedback, implicit feedback has the characteristics of large quantity, easier tracking and easier access. Even in many practical application scenarios of the recommendation system, there is only implicit feedback without explicit feedback Based on implicit feedback to recommend there has been a problem, that is usually implicit feedback provides only reflect the noise signal of user preferences, because although the observed interactions at least reflect the user’s interest in the item, but not observed the interaction may be missing data, and is not necessarily negative samples, so it’s natural lack of negative feedback In this work, this paper improves the neural colla-borative filtering algorithm (NCF) by extracting the popularity characteristics as the basis for extracting negative samples from implicit feedback data. To a certain extent, it solves the problem that negative sampling introduces a lot of noise in previous work. At the same time, we found that the gradient vanishing in the training process when using the traditional loss function Binary Cross Entropy (BCE). In order to solve this problem, a new loss function, BCE-Max, was proposed in this paper. Based on the above improvements, we proposed a neural collaborative filtering recommendation algorithm (PNCF) based on popularity characteristics. By comparing the performance of PNCF, NCF, traditional collaborative filtering and some matrix factorization recommendation algorithms in the evaluation indexes such as HR@10, NDCG@10 and avg_popularity@10, we find that PNCF has certain advantages over these recommendation algorithms. Meanwhile, We found that compared with NCF, the experimental results of PNCF improved by about 1.2% and 2% onHR@10 and NDCG@10 respectively, and even improved by about 2% on HR@1 and NDCG@I.

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

Recommendation systems have been the backbone of the digital age, helping users choose relevant information from the pool of data. However, modern recommendation engines have an inherent flaw in the way they take user feedback. The implicit feedback, like history, can be misleading and might not provide the true sentiment of the user, and explicit feedback is incoherent. This paper aims to tackle this flaw by integrating user sentiment into recommender systems using ECG signals from a wearable device. Physiological signals like ECG have been known to contain the emotional information of the user. We propose a combination of classification and recommendation, wherein the AdaBoost classifier uses ECG signals from a wearable device for emotion classification into positive or negative feedback depending on the valence-arousal values, with an accuracy of 0.98. This feedback is then fed to the recommender engine as implicit feedback of the user giving precision and recall of 0.72 and 0.68, respectively. Therefore, the proposed approach allows for accurate and seamless implicit feedback that incorporates user emotions in recommendation systems.

Recommendation with implicit feedback has been extensively studied in recent years. It is more difficult to provide users with stable and accurate recommendation compared to recommendation with explicit feedback, due to the reason that interactions from implicit feedback datasets do not clearly indicate the level of user preference. Most existing methods dealing with implicit feedback have achieved excellent performance by focusing on other aspects rather than directly inferring user preference. In this paper, we offer accurate recommendation to users by addressing the problem of directly inferring user preference from implicit feedback with such less information and huge uncertainty. We propose a novel mixture-preference model (MPBMF), which introduces a set of pseudo-preference values to surmise the true user preference. More specifically our proposed model can be described as a Gaussian mixture model in which each single model is trained with pseudo-preferences which show the user's different views for items. Then the predicted user preference is estimated by the models under different pseudo-preferences with different contributions. We conduct extensive experiments on three real-world datasets, and the superior performance demonstrates the effectiveness of our model.

Writing has been the most difficult skill among EFL students for several decades. It inevitably promotes writing feedback and approaches to English writing classroom to minimize students’ errors in their writing draft revision. Hereby, the current study aimed at investigating perceptions towards the three writing features: vocabulary, grammar, and content, and examining the differences of the three assessments including teacher feedback, peer feedback, and self-correction. In addition, the teacher feedback preference as implicit and explicit feedback was determined as well. Participants were 32 first-year undergraduate students majoring in English for International Communication at Rajamangala University of Technology, Lanna Tak, Thailand. The current study employed a mixed-method research approach. Questionnaires and open-ended questions were utilized as research tools. Participants were assigned three genres of writing paragraphs. It took 15 consecutive weeks in providing three different feedback to purposive samples. For every assignment, their peers corrected their first drafts and then they rechecked and edited their output by their own decision. Afterward, the teacher provided both implicit and explicit feedback on the revision process. The data obtained were quantitatively analyzed for mean, standard deviation, and a paired sample t-test which have been deployed to the differences among the three feedback. Correspondingly, all written responses were thematically grouped and transcribed into frequency and percentage. The findings indicated that students mostly expected the teacher to edit their misused words, grammatical errors, and ideas on their drafts. As for the three feedback, most beginning writers particularly believed that teacher feedback, which was followed by self-correction and peer feedback, was much necessary for writing improvement and teachers should edit their redrafts explicitly in an EFL writing classroom.

This study aimed firstly to investigate perception on spoken error feedback of English speaking Korean learners and their teachers, and that of Chinese speaking Korean learners and their teachers, secondly to compare them to figure out the best type of feedback. There was no big difference between the learner groups except `who corrected errors.` All learners and teachers agreed strongly to receive spoken error feedback. Both American learners and Chinese learners perceived to receive spoken error feedback more frequently than teachers did. The best timing for feedback was perceived to be `after I speak`. Teachers focused on serious and frequent errors to be corrected while learners took every error to be corrected regardless of its seriousness and frequency. There was discrepancy of perception on ways of feedback between teachers and learners. Learners perceived direct way of correction effectively such as explicit feedback and meta-linguistic feedback whereas teachers did indirect way of correction more effectively such as implicit feedback and repetition. The biggest discrepancy was found in explicit feedback. However, both learners and teachers perceived confirmation check very effectively. Both American learners and Chinese learners preferred to be corrected firstly by native Korean teachers, but secondly American learners preferred themselves differently from Chinese learners who preferred Chinese speaking Korean teachers. (Kyunghee University)

In this study it was attempted to investigate whether different CF strategies, including implicit, explicit, and emergent CF, can differently affect the accuracy and fluency of oral production among Iranian EFL learners. In addition, it explored the learners' attitudes towards how they felt about the CF types applied in the classroom. For these purposes, the researchers selected 54 homogeneous pre-intermediate learners on the basis of the PET results and randomly assigned them into three experimental groups: the Implicit group, the Explicit group, and the Emergent group. While the Implicit and Explicit groups received just implicit feedback and explicit correction for their erroneous oral production, respectively, the Emergent group took CF from implicit to explicit. Oral narrative and picture description tasks and semi-structured interviews were used to collect the quantitative and qualitative data for the study. The results of inferential statistics indicated significant differences among the feedback types in both past-tense and future-tense accuracy. Moreover, the Emergent group had a better performance compared with the other groups with respect to accuracy. However, no significant difference was revealed among the feedback types with respect to fluency. The results of the content analysis also indicated that the learners mostly preferred to receive emergent feedback and participate in the process of error correction. The findings of this study can raise researchers', teachers', and teacher trainers' awareness of the function of various CF types.

We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations. By analyzing their explicit and implicit feedback, we show that exposure to specific levels of music recommendation diversity may be responsible for long-term impacts on listeners’ attitudes. In particular, we highlight the function of diversity in increasing the openness in listening to EM, a music genre not particularly known or liked by the participants previous to their participation in the study. Moreover, we demonstrate that recommendations may help listeners in removing positive and negative attachments towards EM, deconstructing pre-existing implicit associations but also stereotypes associated with this music. In addition, our results show the significant influence that recommendation diversity has in generating curiosity in listeners.

Movie collaborative filtering with multiplex implicit feedbacks

Since the emergence of the process-oriented approach to second language writing instruction two main questions have been what and how error feedback should be given to the students. The question of whether teachers should provide feedback on grammar in the writing assignments of English as a foreign language students, and if so how, has been a matter of considerable debate in the field of second language writing. The present study investigated the possible effect of implicit and explicit corrective feedback on descriptive writing accuracy of Iranian intermediate EFL learners (N=39) in Hamedan Islamic Azad University. Two groups were selected: explicit corrective feedback group (N=22) and implicit corrective feedback group (N=17). They received corrective feedback on three grammatical structures. The results showed that written corrective feedback can lead to writing accuracy improvement in the short-term, but it may be unhelpful in the long-run.