<prompt> Akaretsa lintlafatso tsa morao-rao lefapheng la litsamaiso tsa khothaletso.nan  <response>: Instruction in English: Summarize the recent developments in the field of recommender systems. . Response in English: Recommender systems have seen immense growth and development in the recent past. One of the major developments is the shift from traditional Collaborative Filtering to more advanced Deep Learning techniques to generate recommendations. The integration of contextual information and personalization through the use of hybrid recommendation techniques has also improved the performance of these systems. Another major development has been the incorporation of real-time recommendation updates through the use of online learning algorithms. This enables the system to learn from new data and adjust its recommendations accordingly. Moreover, with the advent of the internet and the proliferation of data, there has been an increased focus on developing scalable recommender systems that can handle large volumes of data. Overall, there continues to be a focus on research and development in the field of recommender systems with the primary goal of improving the quality and relevance of recommendations. . Response in Sesotho: Litsamaiso tsa likhothaletso li bone kholo le ntsetsopele e kholo nakong e fetileng. E ngoe ea lintlafatso tse kholo ke ho tloha ho Sefe ea Tšebelisano-moho ea setso ho ea mekhoeng e tsoetseng pele ea ho Ithuta e Tebileng ho hlahisa likhothaletso. Ho kopanngoa ha tlhahisoleseding ea maemo le ho iketsetsa motho ka ho sebelisa mekhoa ea likhothaletso tse nyalisitsoeng ho boetse ho ntlafalitse tsebetso ea litsamaiso tsena. Khatelo-pele e ngoe e kholo e bile ho kenyelletsoa ha lintlafatso tsa nako ea nnete ka tšebeliso ea li-algorithms tsa ho ithuta inthaneteng. Sena se nolofalletsa sistimi ho ithuta ho data e ncha le ho lokisa likhothaletso tsa eona ka nepo. Ho feta moo, ka ho qaleha ha marang-rang le ho ata ha data, ho bile le ho tsepamisa maikutlo ho eketsehileng ho ntsetsopele mekhoa ea li-scalable recommender tse khonang ho sebetsana le boitsebiso bo bongata. Ka kakaretso, ho ntse ho tsoela pele ho tsepamisa maikutlo ho lipatlisiso le ntsetsopele lefapheng la litsamaiso tsa li-compoter ka sepheo sa mantlha sa ho ntlafatsa boleng le bohlokoa ba likhothaletso. 