The field of personalized product recommendation systems has seen tremendous growth in recent years. The usefulness of the algorithms’ abilities to filter out data from vast sets has been shown to be crucial in today’s information-heavy online experience. Our goal is therefore to compare two recommender models, one based on Random Indexing, the other on Collaborative Filtering, in order to find out if one is better suited to the task than the other. We bring up relevant previous research to set the context for our study, its limitations and possibilities. We then explain the theories, models and algorithms underlying our two recommender systems and finally we evaluate them, partly through empirical data collection from our employer Kavall’s platform, and partly through analysing data from interviews. We judge that our study is scientifically relevant as it compares an algorithm that is rarely used in this context, Random Indexing, to a more established recommendation algorithm, Collaborative Filtering, and as such the result of this comparison might give useful insights into the further development of new or existing algorithms. While more testing is required, the study did show signs that Random Indexing does have the potential of outperforming Collaborative Filtering in some areas, and further development of the model might be a worthwhile endeavor. / Området för personliga produktrekommendationer har sett en enorm tillväxt under de senaste Åren. Användbarheten av algoritmernas förmåga att filtrera ut data ur stora uppsättningar har visat sig vara avgörande i dagens informationstunga onlineupplevelse. Vårt mål Är därför att jämföra två rekommendatormodeller, en baserad på Random Indexing, den andra på Collaborative Filtering, för att ta reda på om den ena Är bättre lämpad för uppgiften Än den andra. Vi tar upp relevant tidigare forskning för att sätta sammanhanget för vår studie, dess begränsningar och möjligheter. Vi förklarar sedan de teorier, modeller och algoritmer som ligger till grund för våra två rekommendationssystem och slutligen utvärderar vi dem, dels genom empirisk datainsamling från vår arbetsgivare Kavalls plattform, dels genom att analysera data från intervjuer. Vi bedömer att vår studie Är vetenskapligt relevant då den jämför en algoritm som sällan används i detta sammanhang, Random Indexing, med en mer etablerad rekommendationsalgoritm, Collaborative Filtering, och som sådan kan resultatet av denna jämförelse ge användbara insikter i den fortsatta utvecklingen av nya eller befintliga algoritmer. även om fler tester krävs, visade studien tecken på att Random Indexing har potentialen att överträffa Collaborative Filtering på vissa områden, och vidareutveckling av modellen kan vara ett givande åtagande.

With the rapid growth of mobile commerce (m-commerce), understanding consumer behaviour in online shopping contexts has become increasingly vital for marketers and retailers. This study examines the influence of product recommendations on consumers' impulse buying behaviour and post-purchase emotions within m-commerce. Employing a quantitative survey method, the study investigates the experiences and perceptions of Swedish online shoppers.  The findings reveal significant insights into how product recommendations impact impulse buying behaviour. Specifically, product recommendations are primarily driven by hedonic motives. However, the relationship between impulse buying and post-purchase emotions is complex, with varied emotional responses observed. While some consumers experience heightened satisfaction, others grapple with feelings of dissatisfaction or regret. The interplay between hedonic and utilitarian motivations, alongside cognitive processes like cognitive dissonance, further complicates the picture. Contrary to expectations, neither hedonic nor utilitarian motives significantly correlate with post-purchase emotions, highlighting the influence of economic considerations and cognitive processes. The study underscores the multifaceted nature of consumer emotions and behaviours in online shopping contexts, emphasising the importance of considering internal and external factors. This study contributes theoretical and practical insights into consumer behaviour in online shopping while paving the way for future research endeavours in understanding the complexities of impulse buying and post-purchase emotions in m-commerce.