Maus is not just the story of one family’s tragedy during the Holocaust. It is also Art Spiegelman’s memoir, his dealings with his father. While it is very clear that this graphic novel is autobiographical, even fiction writers put their own lives into their texts in varied ways. Aside from Maus, what text that we studied this semester did you find most autobiographical? In your answer, you should incorporate details from the author’s life as well as how those details manifested themselves in their story/poetry/novel/play.
Be sure to cite your source(s) for information—but do not use Wikipedia.
This essay will consist of two sections. In the first section, you will discuss the text you liked the best; the second section will deal with the text you liked the least. But here’s the catch. You are not to base your answer on the artistic merits of the text, but on your own life. What did the poem/song/story/etc touch upon in your own life that made you like it or dislike it?
Sample Solution
recommendations by combining user feedback on items with the content (i.e., features) associated with them (Lops, De Gemmis, & Semeraro, 2011). Operating by the logic that users will prefer the same features in movies as they do in movie trailers, the following research question is proposed: “How can recommendation systems guide the generation of personalized movie trailers, and how effective is personalization in movie trailers?” The purpose of this study is to propose a framework for personalized movie trailers based on consumer preferences, and to provide empirical evidence for the effectiveness of such a trailer for audiences. 1.3 Academic relevance Numerous studies in the field of video summarization or video abstraction have been conducted. Video summarization systems select significant segments for users to generate a short version of a lengthy video (Kannan, Ghinea, & Swaminathan, 2015). Existing approaches can be classified into cognitive-level and affective-level approaches, the former extracting low-level features such as color, motion, and composition, and the latter utilizing high-level semantic features such as events and semantic concepts. Most of these approaches focus on analyzing genre-specific movie trailers for patterns to automatically select salient moments in a movie for a movie trailer (Hermes & Schultz, 2006; Smeaton, Lehane, O’Connor, Brady, & Craig, 2006; Smith, Joshi, Huet, Hsu, & Cota, 2017), thus generating a generic summary that is common for all users.>
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