Multidimensional Factor and Cluster Analysis Versus Embedding-Based Learning for Personalized Supermarket Offer Recommendations

Stalidis, George; Siomos, Theodosios; Kaplanoglou, Pantelis I.; Katsalis, Alkiviadis; Karaveli, Iphigenia; Delianidi, Marina; Diamantaras, Konstantinos I.

doi:10.1007/978-3-030-60104-1_30

Cited by 1 publication

(3 citation statements)

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“…A smaller list length may result in higher precision because the RS will only recommend the few best matches with the highest probability of being correct. In order to have a clear view of an algorithm's abilities, it is common to measure precision and recall at several different values for k. It is worth mentioning that in some RSs, the length of the recommendation list can be left flexible for the algorithm to decide per case so that the RS recommends as many items as it considers "good matches" based on a threshold to a user-item distance measure [38]. In such cases, an overall precision, recall, and/or F-measure can be evaluated at variable k.…”

Section: Evaluating Recommendation Listsmentioning

confidence: 99%

“…They are the preferred technologies for modeling multiple-step behavior and for extracting features from unstructured data. [38, Markovian methods Less popular methods that aim at capturing the sequences of user actions in click stream data and session-based RS. Recent research work was limited.…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

“…Their effort was to improve personalized recommendations within user sessions by adding diversity and reducing the dependence of the result from the strict order of the user's item selection sequence. Stalidis et al [38] applied an embedding-based algorithm on e-grocery RS, comparing it with a statistical factor and clustering method. It was found that the NN approach achieved comparable precision with the statistical method and, at the same time, offered the ability to overcome the limitations of the latter to be applied on large-scale problems.…”

Section: Shallow Neural Modelsmentioning

confidence: 99%

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Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

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In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

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Section: Evaluating Recommendation Listsmentioning

confidence: 99%

Section: Neural Network and Deep Learningmentioning

confidence: 99%

Section: Shallow Neural Modelsmentioning

confidence: 99%

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