Statistical analysis of kk-nearest neighbor collaborative recommendation

Collaborative recommendation is an information-filtering technique that attempts to present information items that are likely of interest to an Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists which would allow us to precisely describe the mathematical forces driving collaborative filtering. To provide an initial contribution to this, we propose to set out a general sequential stochastic model for collaborative recommendation. We offer an in-depth analysis of the so-called cosine-type nearest neighbor collaborative method, which is one of the most widely used algorithms in collaborative filtering, and analyze its asymptotic performance as the number of users grows. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided.Comment: Published in at http://dx.doi.org/10.1214/09-AOS759 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

From Consumer Preferences Towards Buying Decisions - Conjoint Analysis as Preference Measuring Method in Product Recommender Systems

This paper briefly introduces the conjoint analysis as a method to measure consumer preferences. Based on the introduction the conjoint analysis is suggested as preference measuring method in product recommender systems. The challenges and limits in applying the conjoint analysis to product recommender systems are analysed and discussed. In the end we present a set of adaptations to the traditional conjoint analysis which address the mentioned challenges and limits

UNICON: A unified framework for behavior-based consumer segmentation in e-commerce

Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience

Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery

Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain

Filter Bubble vs. Preference Bubble

Tämän opinnäytetyön aiheena oli internetin personointi ja siitä aiheutuva filter bubble –ilmiö. Tarkoituksena oli tutkia kuluttajien suhtautumista ilmiöön, jota Suomessa ei vielä tunnisteta laajasti. Suhtautuminen haluttiin tuoda esiin vastakkainasettelun avulla. Filter bubble –näkökulma edusti tässä työssä ilmiön negatiivista suhtautumistapaa ja preference bubble –näkökulma positiivista. Opinnäytetyö oli tietopaketti yrityksille Filter bubble –ilmiön ominaisuuksista sekä sen käyttäytymisestä eri toimintaympäristöissä kuten Google ja Facebook. Työ oli osa Lau-reaammattikorkeakoulun ja Kurio Oy:n yhteistyössä toteuttamaa Kuluttajakäyttäytymisen digitaalisuus -hanketta. Kuluttajien suhtautumista pyrittiin selvittämään laadullisen tutkimuksen avulla. Pääongelmana oli selvittää kuluttajien suhtautuminen internetin personointiin ja sen aiheuttamaan filter bubble –ilmiöön: koettiinko se hyvänä vai huonona. Alaongelmien avulla pyrittiin selvittämään, oliko ilmiö tuttu kuluttajille ja miten siihen reagoitiin sekä kokiko kuluttaja itse olevansa kuplassa. Näkökulmana opinnäytetyössä toimi markkinointi. Opinnäytetyön teoriaosuudessa syvennyttiin filter bubble -ilmiön lähtökohtiin ja miten ja missä se toimii. Seuraavaksi esiteltiin suhtautumisnäkökulmat ”filter bubble” ja ”preference bubble”, jotka pohjautuivat kirjallisuuteen. Lisäksi teoriaosuudessa esiteltiin suomalaiset kuluttajat ja filter bubble –ilmiön toimintaympäristöt yksitellen. Tässä opinnäytetyössä käytettiin laadullisina tutkimusmenetelminä teemahaastattelua ja eläyty-mismenetelmää ilmiön tutkimiseen ja ymmärtämiseen. Tavoitteena oli saavuttaa syvällinen ja mahdollisimman laaja ymmärrys ilmiöstä. Sen lisäksi tavoitteena oli selvittää tutkittavien ajattelun logiikkaa, johon eläytymismenetelmä sopi parhaiten. Tutkimusten tulosten perusteella kuluttajien suhtautuminen internetin personointiin oli filter bubble –näkökulman mukainen. Tuloksissa korostui epäluuloisuus tietojenkeräystä kohtaan ja epäluottamus Googlen toimintaan. Haastateltavat, jotka valitsivat filter bubble –näkökulman, eivät olleet aktiivisia internetin käyttäjiä. Tutkimustuloksista pääteltiin, ettei filter bubble –ilmiö muodostunut oikein aiheuttaen epäonnistumisia kohdennettavuudessa. Haastateltavat, jotka valitsivat preference bubble –näkökulman, olivat aktiivisia internetin käyttä-jiä ja tutkittavista nuorimpia. Aktiivisuus internetissä mahdollisti suuren informaation tarjonnan ilmiölle, jolloin myös kupla muodostui kuluttajan näköiseksi ja oikea kohdennettavuus onnistui. Jatkotutkimuksena suositeltiin filter bubble –ilmiön tutkimista eri päätelaitteissa. Tänä päivänä ihmiset käyttävät monia eri päätelaitteita eri aikaan päivästä ja erilaiseen internettoimintaan. Mielenkiintoista olisi tutkia, kuinka tämä seikka vaikuttaa filter bubble –ilmiön muodostumiseen, ja muodostuuko esimerkiksi yhdelle kuluttajalle monta eri persoonaa monen eri päätelaitteen vuoksi.In this thesis the subject was the filter bubble, which was created by the internet’s personalization. The purpose was to research consumers’ attitude towards the filter bubble, which was not well-known in Finland. The attitudes were brought to light by putting two different viewpoints against each other. In this thesis the filter bubble aspect represented a negative viewpoint as for the preference bubble aspect represented a positive viewpoint. The filter bubble was examined in its operational environment, which concluded online stores, Facebook and Google. The goals of this thesis were to present the filter bubble entirely and to bring new information about its features and how to exploit them. This thesis was part of the Consumer Behavior in Digital Environment project that was made in collaboration with Laurea University of Applied Sciences and Kurio Oy. Consumer behavior was examined by qualitative research, which was a great way to get broad and deep information of the subject. The main objectives were to examine consumer behavior towards the internet’s personalization and to find out, was the filter bubble perceived negative or positive. The questions that followed were “Was the filter bubble known by consumers?”, “How did they perceive it?” and “Did they feel like they’re in a bubble?” The viewpoint in the research made in this thesis, was marketing. In the theory chapters of this thesis, the filter bubble’s roots were presented and how did the “bubble” work. After that the filter bubble aspect and the preference bubble aspect were explained. In addition, the Finnish consumers were displayed and also the operational environment of the bubble. The research methods used in this thesis were theme interview and empathize method. They were used to get deeper knowledge of the phenomenon. The goal was to understand the phenomenon and the way it worked. Another goal was to understand the examinee’s logical thinking in which the emphasize method was useful. The results of the research concluded that the consumers perceived the internet’s personalization from the filter bubble aspect. In the results the internet’s personalization wasn’t found useful, because of the gathering of information was experienced as suspicious activity. The information gathering lead by Google was experienced as unethical and unnecessary. The consumers which chose the filter bubble aspect weren’t active users of the internet. This is why it was concluded that the filter bubble wasn’t formed correctly leading to bad experiences. Individuals which chose the preference bubble aspect were young and active internet users. Being active in the internet gives the filter bubble a better chance of forming correctly and representing the individual whom it’s serving. In further research it was recommended to study the effects of multiple devices on the formation of the filter bubble. In this day and age people use many devices for many reasons at a different time of the day. How would this affect the formation of the bubble and could it be, that one person always has a different persona on a different kind of device

Design and Implementation of a Customer Personalised Recomender System

[ANGLÈS] Market basket analysis is examined through the application of probabilistic topic models and case-based reasoning in order to provide more insight into customer buying habits and generate meaningful recommendations