Boost Cross-selling With Recommender Systems for Retail Industry Using AI

Dec 13, 2023

0 likes | 11 Views

In the retail revolution, AI-driven recommender systems stand as beacons of precision. These systems, powered by advanced algorithms, analyze vast datasets, unraveling customer preferences and behaviors. Tailoring recommendations with each click, they transform the shopping experience. For retailers, this means increased sales and loyalty. Customers benefit from time-saving, personalized suggestions, elevating satisfaction. The dynamic synergy between AI and retail paints a future where every click refines and defines the personalized journey, revolutionizing the retail landscape.

Share Presentation

Embed Code

Link

Zoe15

Download Presentation

Boost Cross-selling With Recommender Systems for Retail Industry Using AI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Recommender systems

Recommender systems. Ram Akella November 26 th 2008. Outline. Types of recommendation systems Search-based recommendations Category-based recommendations Collaborative filtering Clustering Association Rules Information filtering Classifiers. Types of recommendation systems.

651 views • 31 slides

Recommender Systems

Recommender Systems. Dr. Frank McCown Intro to Web Science Harding University. This work is licensed under Creative Commons Attribution- NonCommercial 3.0. Image: http://lifehacker.com/5642050/five-best-movie-recommendation-services. Recommender Systems.

554 views • 31 slides

Recommender Systems

Recommender Systems . Based Rajaraman and Ullman: Mining Massive Data Sets & Francesco Ricci et al. Recommender Systems Handbook. . Recommender System. All of these thrive on User Generated Content (UGC)!. Recommender System. Central Theme : Predict ratings for unrated items

527 views • 19 slides

Recommender Systems

Recommender Systems. Eric Nalisnick CSE 435. …. How can businesses direct customers to groups of similar , interesting , relevant , and undiscovered items? . Recommender Systems!. Method #1: Memory-Based Collaborative Filtering. A. B. C. D. E. 0. 1. =. 0. C. 0.

656 views • 44 slides

Recommender Systems

Sumbul Jahan. Recommender Systems. This world is an over-crowded place. They all want to get our attention. But we need a few of them!. Something which is popular. Something which is of our interest. Something which is liked by people of our community. What we are looking for?.

288 views • 19 slides

Recommender Systems

Recommender Systems. Presented By: Madiha Saleem Sunniya Rizvi. Collaborative Filtering. Collaborative filtering is a technique used by recommender systems to combine different users' opinions and tastes in order to achieve personalized recommendations. There are 3 approaches:

466 views • 27 slides

Recommender Systems

Recommender Systems. Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009. Recommender Systems. Outline Introduction motivation, applications, issues Collaborative filtering user-based, item-based, challenges

934 views • 56 slides

Recommender systems

Recommender systems. IR vs. RS (Rocha, 2001). IR – pull RS – push – recognize users  conversation  adaptation. Approaches for Recommender Systems. C ontent-based C ollaborative recommendation s S tructural recommendation s C ollective recommendation s Hybrid recommendations.

347 views • 19 slides

Recommender Systems

Recommender Systems. Recommender Systems. In many cases, users are faced with a wealth of products and information from which they can choose. To alleviate this many web sites help users by using Recommender Systems, List of items or page that are likely to interest them

807 views • 34 slides

Recommender Systems

Recommender Systems. Collaborative Filtering Process. Challenge - Sparsity. Active users may have purchased well under 1% of the items (1% of 2 million books is 20,000 books). Solution : Use sparse representations of the rating matrix. Ratings in a hashtable. critics = {

395 views • 22 slides

Recommender Systems

Recommender Systems. >1,000,000,000. Finding Trusted Information. How many cows in Texas?. http://www.cowabduction.com/. Outline. What are Recommender Systems? How do they work? How can we integrate social information / trust? What are some applications?. Netflix. Amazon.

578 views • 34 slides

Recommender Systems

Recommender Systems. Collaborative Filtering & Content-Based Recommending. Recommender Systems. Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

634 views • 36 slides

Recommender Systems

Recommender Systems. Av: Knut Marius Sundt Veileder: Thomas Ågotnes. Recommender Systems. Hva? Informasjonsfiltrering Personifisert Muligheter ≈∞ Hvorfor? Store mengder data Effektivitet (tid er penger) Tjene på andres erfaringer Målrettet markedsføring. Recommender Systems.

309 views • 8 slides

Recommender Systems

Recommender Systems. Customization. Customization is one of the more attractive features of electronic commerce. Creating a different product for every user, suited to his/her tastes. Once thought to be a novelty, now essential

389 views • 29 slides

Recommender systems

Recommender systems. Drew Culbert IST 497 12/12/02. Overview. Definition Ways its used Problems Maintenance The future. What is it?. Recommender systems are a technological proxy for a social process.

381 views • 19 slides

Recommender Systems

Recommender Systems. Collaborative Filtering & Content-Based Recommending Lecture 13. Recommender Systems. Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

496 views • 47 slides

Recommender Systems

495 views • 47 slides

Recommender Systems

Recommender Systems. Finding Trusted Information. How many cows in Texas?. http://www.cowabduction.com/. Outline. What are Recommender Systems? How do they work? How can we integrate social information / trust? What are some applications?. Netflix. Amazon. How do they work? .

366 views • 33 slides

Recommender Systems

Recommender Systems. Robin Burke DePaul University Chicago, IL. About myself. PhD 1993 Northwestern University Intelligent Multimedia Retrieval 1993-1998 Post-doc at University of Chicago Kristian Hammond Helped found Recommender, Inc. became Verb, Inc. 1998-2000

845 views • 74 slides

More Related